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tests/testthat/_snaps/ALE-categorical.md
In ale: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)
bootstrapped binary outcome with full 1D and 2D ALE
Code
unclass(cat_cars_ale)
Output
<object>
attr(,"S7_class")
<ale::ALE> class
@ parent : <S7_object>
@ constructor: function(model, x_cols, data, y_col, ..., exclude_cols, parallel, model_packages, output_stats, output_boot_data, pred_fun, pred_type, p_values, aler_alpha, max_num_bins, boot_it, boot_alpha, boot_centre, seed, y_type, sample_size, silent, .bins) {...}
@ validator : <NULL>
@ properties :
$ effect: <list>
$ params: <list>
attr(,"effect")
attr(,"effect")$Asia
attr(,"effect")$Asia$ale
attr(,"effect")$Asia$ale$d1
attr(,"effect")$Asia$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$Asia$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
attr(,"effect")$Asia$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
attr(,"effect")$Asia$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
attr(,"effect")$Asia$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
attr(,"effect")$Asia$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
attr(,"effect")$Asia$ale$d2
attr(,"effect")$Asia$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$Asia$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$Asia$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$Asia$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$Asia$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$Asia$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
attr(,"effect")$Asia$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
attr(,"effect")$Asia$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
attr(,"effect")$Asia$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
attr(,"effect")$Asia$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
attr(,"effect")$Asia$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
attr(,"effect")$Asia$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
attr(,"effect")$Asia$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
attr(,"effect")$Asia$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
attr(,"effect")$Asia$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
attr(,"effect")$Asia$stats
attr(,"effect")$Asia$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$Asia$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$Asia$boot_data
attr(,"effect")$Asia$boot_data$d1
attr(,"effect")$Asia$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$Asia$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$Asia$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE -0.0432 38 -0.0432 -0.0432
2 0 TRUE 0.0632 26 0.0632 0.0632
3 1 FALSE 0.0321 38 0.0321 0.0321
4 1 TRUE -0.0101 26 -0.0101 -0.0101
5 2 FALSE 0.0297 38 0.0297 0.0297
6 2 TRUE -0.00670 26 -0.00670 -0.00670
attr(,"effect")$Asia$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental -0.305 2 -0.305 -0.305
2 0 Cadillac Fleetwood -0.305 2 -0.305 -0.305
3 0 Merc 450SL -0.305 2 -0.305 -0.305
4 0 Merc 450SLC -0.305 2 -0.305 -0.305
5 0 Camaro Z28 -0.305 2 -0.305 -0.305
6 0 Chrysler Imperial -0.305 2 -0.305 -0.305
7 0 Duster 360 -0.305 2 -0.305 -0.305
8 0 Merc 450SE -0.305 2 -0.305 -0.305
9 0 Hornet Sportabout -0.305 2 -0.305 -0.305
10 0 AMC Javelin -0.305 2 -0.305 -0.305
# i 86 more rows
attr(,"effect")$Asia$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.118 30 0.118 0.118
2 0 four 0.0718 24 0.0718 0.0718
3 0 five -0.526 10 -0.526 -0.526
4 1 three 0.0530 30 0.0530 0.0530
5 1 four -0.162 24 -0.162 -0.162
6 1 five -0.460 10 -0.460 -0.460
7 2 three 0.184 30 0.184 0.184
8 2 four 0.0960 24 0.0960 0.0960
9 2 five -0.00410 10 -0.00410 -0.00410
attr(,"effect")$Asia$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 0.00549 14 0.00549 0.00549
2 0 2 -0.00191 19 -0.00191 -0.00191
3 0 3 -0.00191 9 -0.00191 -0.00191
4 0 4 -0.00191 16 -0.00191 -0.00191
5 0 8 -0.00191 6 -0.00191 -0.00191
6 1 1 0.00549 18 0.00549 0.00549
7 1 2 -0.00377 16 -0.00377 -0.00377
8 1 3 -0.00377 13 -0.00377 -0.00377
9 1 4 -0.00377 15 -0.00377 -0.00377
10 1 8 -0.00377 2 -0.00377 -0.00377
11 2 1 0.00549 15 0.00549 0.00549
12 2 2 -0.0000232 13 -0.0000232 -0.0000232
13 2 3 -0.0000232 13 -0.0000232 -0.0000232
14 2 4 -0.0000232 20 -0.0000232 -0.0000232
15 2 8 -0.0000232 3 -0.0000232 -0.0000232
attr(,"effect")$Asia$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 0.0000319 1 0.0000319 0.0000319
2 0 1.94 0.00000854 7 0.00000854 0.00000854
3 0 2.46 -0.00000364 7 -0.00000364 -0.00000364
4 0 2.79 -0.00000366 7 -0.00000366 -0.00000366
5 0 3.19 -0.00000366 7 -0.00000366 -0.00000366
6 0 3.44 -0.00000366 7 -0.00000366 -0.00000366
7 0 3.52 -0.00000366 7 -0.00000366 -0.00000366
8 0 3.73 -0.00000366 7 -0.00000366 -0.00000366
9 0 4.05 -0.00000366 7 -0.00000366 -0.00000366
10 0 5.45 -0.00000366 7 -0.00000366 -0.00000366
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2
attr(,"effect")$Asia$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$Asia$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$Asia$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0.106 2 0.106 0.106
2 0 TRUE Lincoln Continental 0.106 0 -1.38 -1.38
3 0 FALSE Cadillac Fleetwood 0.106 2 0.106 0.106
4 0 TRUE Cadillac Fleetwood 0.106 0 -1.38 -1.38
5 0 FALSE Merc 450SL 0.106 2 0.106 0.106
6 0 TRUE Merc 450SL 0.106 0 -1.38 -1.38
7 0 FALSE Merc 450SLC 0.106 2 0.106 0.106
8 0 TRUE Merc 450SLC 0.106 0 -1.38 -1.38
9 0 FALSE Camaro Z28 0.106 2 0.106 0.106
10 0 TRUE Camaro Z28 0.106 0 -1.38 -1.38
# i 182 more rows
attr(,"effect")$Asia$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.0688 30 -0.0688 -0.0688
2 0 TRUE three -0.0688 0 0.453 0.453
3 0 FALSE four -0.0688 8 -0.0688 -0.0688
4 0 TRUE four -0.466 16 0.0563 0.0563
5 0 FALSE five -0.0688 0 -0.0688 -0.0688
6 0 TRUE five -1.06 10 -0.541 -0.541
7 1 FALSE three -0.204 30 -0.204 -0.204
8 1 TRUE three -0.204 0 -0.104 -0.104
9 1 FALSE four -0.340 8 -0.340 -0.340
10 1 TRUE four -0.607 16 -0.507 -0.507
11 1 FALSE five -0.340 0 -0.340 -0.340
12 1 TRUE five -0.905 10 -0.806 -0.806
13 2 FALSE three -0.00554 30 -0.00554 -0.00554
14 2 TRUE three -0.00554 0 -0.628 -0.628
15 2 FALSE four -0.165 8 -0.165 -0.165
16 2 TRUE four -0.228 16 -0.850 -0.850
17 2 FALSE five -0.165 0 -0.165 -0.165
18 2 TRUE five -0.328 10 -0.950 -0.950
attr(,"effect")$Asia$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0.0166 6 0.0166 0.0166
2 0 TRUE 1 0.0166 8 -0.107 -0.107
3 0 FALSE 2 0.0166 12 -0.0662 -0.0662
4 0 TRUE 2 -0.00350 7 -0.213 -0.213
5 0 FALSE 3 0.0166 7 0.0203 0.0203
6 0 TRUE 3 -0.00342 2 -0.127 -0.127
7 0 FALSE 4 0.0166 11 -0.0662 -0.0662
8 0 TRUE 4 0.550 5 0.423 0.423
9 0 FALSE 8 0.0166 2 0.0203 0.0203
10 0 TRUE 8 0.550 4 0.340 0.340
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0.0139 0 0.0139 0.0139
2 0 TRUE 1.50 0.0139 1 -0.00322 -0.00322
3 0 FALSE 1.94 0.0139 0 0.00361 0.00361
4 0 TRUE 1.94 0.0138 7 -0.0244 -0.0244
5 0 FALSE 2.46 0.0139 1 -0.00732 -0.00732
6 0 TRUE 2.46 0.0138 6 -0.0245 -0.0245
7 0 FALSE 2.79 0.0139 1 -0.00732 -0.00732
8 0 TRUE 2.79 0.0378 6 0.0207 0.0207
9 0 FALSE 3.19 0.0139 3 0.0139 0.0139
10 0 TRUE 3.19 0.0761 4 0.0487 0.0487
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.184 2 -0.184 -0.184
2 0 Cadillac Fleetwood three -0.184 2 -0.184 -0.184
3 0 Merc 450SL three -0.184 2 -0.184 -0.184
4 0 Merc 450SLC three -0.184 2 -0.184 -0.184
5 0 Camaro Z28 three -0.184 2 -0.184 -0.184
6 0 Chrysler Imperial three -0.184 2 0.274 0.274
7 0 Duster 360 three -0.184 2 0.272 0.272
8 0 Merc 450SE three -0.184 2 0.250 0.250
9 0 Hornet Sportabout three -0.184 2 0.250 0.250
10 0 AMC Javelin three -0.184 2 -0.0198 -0.0198
# i 278 more rows
attr(,"effect")$Asia$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 3.42 0 3.42 3.42
2 0 Cadillac Fleetwood 1 3.42 0 3.42 3.42
3 0 Merc 450SL 1 3.42 0 3.42 3.42
4 0 Merc 450SLC 1 3.42 0 3.42 3.42
5 0 Camaro Z28 1 3.42 0 3.42 3.42
6 0 Chrysler Imperial 1 3.42 0 3.00 3.00
7 0 Duster 360 1 3.42 0 -0.418 -0.418
8 0 Merc 450SE 1 3.42 0 -3.32 -3.32
9 0 Hornet Sportabout 1 3.42 0 -3.74 -3.74
10 0 AMC Javelin 1 3.42 0 -4.12 -4.12
# i 470 more rows
attr(,"effect")$Asia$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.342 0 0.342 0.342
2 0 Cadillac Fleetwood 1.50 0.342 0 0.342 0.342
3 0 Merc 450SL 1.50 0.342 0 0.342 0.342
4 0 Merc 450SLC 1.50 0.342 0 0.342 0.342
5 0 Camaro Z28 1.50 0.342 0 0.342 0.342
6 0 Chrysler Imperial 1.50 0.342 0 0.342 0.342
7 0 Duster 360 1.50 0.342 0 0.284 0.284
8 0 Merc 450SE 1.50 0.342 0 -0.0324 -0.0324
9 0 Hornet Sportabout 1.50 0.342 0 -0.0439 -0.0439
10 0 AMC Javelin 1.50 0.342 0 -0.0520 -0.0520
# i 950 more rows
attr(,"effect")$Asia$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.0284 6 0.0284 0.0284
2 0 four 1 0.0284 8 -0.0211 -0.0211
3 0 five 1 0.0284 0 -0.0245 -0.0245
4 0 three 2 0.0284 8 -0.0766 -0.0766
5 0 four 2 0.162 7 0.0573 0.0573
6 0 five 2 0.130 4 0.130 0.130
7 0 three 3 0.0284 7 -0.0211 -0.0211
8 0 four 3 0.193 2 0.140 0.140
9 0 five 3 0.147 0 0.0422 0.0422
10 0 three 4 0.0284 8 -0.0767 -0.0767
# i 35 more rows
attr(,"effect")$Asia$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 0.0363 0 0.0363 0.0363
2 0 four 1.50 0.0363 0 0.0269 0.0269
3 0 five 1.50 0.0363 1 0.0202 0.0202
4 0 three 1.94 0.0363 0 0.00815 0.00815
5 0 four 1.94 0.0527 6 0.00226 0.00226
6 0 five 1.94 0.0535 1 0.00298 0.00298
7 0 three 2.46 0.0363 1 -0.0141 -0.0141
8 0 four 2.46 0.0599 4 0.00943 0.00943
9 0 five 2.46 0.0786 2 0.0281 0.0281
10 0 three 2.79 0.0363 1 -0.0141 -0.0141
# i 80 more rows
attr(,"effect")$Asia$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.00454 0 0.00454 0.00454
2 0 2 1.50 0.00454 1 -0.0287 -0.0287
3 0 3 1.50 0.00454 0 -0.0230 -0.0230
4 0 4 1.50 0.00454 0 -0.0329 -0.0329
5 0 8 1.50 0.00454 0 -0.0335 -0.0335
6 0 1 1.94 0.00454 4 -0.00377 -0.00377
7 0 2 1.94 0.0244 3 -0.0171 -0.0171
8 0 3 1.94 0.0244 0 -0.00721 -0.00721
9 0 4 1.94 0.0244 0 -0.0171 -0.0171
10 0 8 1.94 0.0244 0 -0.0171 -0.0171
# i 140 more rows
attr(,"effect")$Europe
attr(,"effect")$Europe$ale
attr(,"effect")$Europe$ale$d1
attr(,"effect")$Europe$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$Europe$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
attr(,"effect")$Europe$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
attr(,"effect")$Europe$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
attr(,"effect")$Europe$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
attr(,"effect")$Europe$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
attr(,"effect")$Europe$ale$d2
attr(,"effect")$Europe$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$Europe$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$Europe$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$Europe$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$Europe$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$Europe$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
attr(,"effect")$Europe$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
attr(,"effect")$Europe$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
attr(,"effect")$Europe$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
attr(,"effect")$Europe$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
attr(,"effect")$Europe$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
attr(,"effect")$Europe$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
attr(,"effect")$Europe$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
attr(,"effect")$Europe$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
attr(,"effect")$Europe$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
attr(,"effect")$Europe$stats
attr(,"effect")$Europe$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$Europe$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$Europe$boot_data
attr(,"effect")$Europe$boot_data$d1
attr(,"effect")$Europe$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$Europe$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$Europe$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0.0432 38 0.0432 0.0432
2 0 TRUE -0.0632 26 -0.0632 -0.0632
3 1 FALSE -0.0321 38 -0.0321 -0.0321
4 1 TRUE 0.0101 26 0.0101 0.0101
5 2 FALSE -0.0297 38 -0.0297 -0.0297
6 2 TRUE 0.00670 26 0.00670 0.00670
attr(,"effect")$Europe$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.166 2 0.166 0.166
2 0 Cadillac Fleetwood 0.166 2 0.166 0.166
3 0 Merc 450SL 1.17 2 1.17 1.17
4 0 Merc 450SLC 1.17 2 1.17 1.17
5 0 Camaro Z28 0.166 2 0.166 0.166
6 0 Chrysler Imperial 0.166 2 0.166 0.166
7 0 Duster 360 0.166 2 0.166 0.166
8 0 Merc 450SE 1.17 2 1.17 1.17
9 0 Hornet Sportabout 0.166 2 0.166 0.166
10 0 AMC Javelin 0.166 2 0.166 0.166
# i 86 more rows
attr(,"effect")$Europe$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three -0.372 30 -0.372 -0.372
2 0 four 0.211 24 0.211 0.211
3 0 five 0.610 10 0.610 0.610
4 1 three -0.106 30 -0.106 -0.106
5 1 four 0.394 24 0.394 0.394
6 1 five 0.594 10 0.594 0.594
7 2 three -0.239 30 -0.239 -0.239
8 2 four 0.136 24 0.136 0.136
9 2 five -0.0638 10 -0.0638 -0.0638
attr(,"effect")$Europe$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 -0.00549 14 -0.00549 -0.00549
2 0 2 0.00191 19 0.00191 0.00191
3 0 3 0.00191 9 0.00191 0.00191
4 0 4 0.00191 16 0.00191 0.00191
5 0 8 0.00191 6 0.00191 0.00191
6 1 1 -0.00549 18 -0.00549 -0.00549
7 1 2 0.00377 16 0.00377 0.00377
8 1 3 0.00377 13 0.00377 0.00377
9 1 4 0.00377 15 0.00377 0.00377
10 1 8 0.00377 2 0.00377 0.00377
11 2 1 -0.00549 15 -0.00549 -0.00549
12 2 2 0.0000232 13 0.0000232 0.0000232
13 2 3 0.0000232 13 0.0000232 0.0000232
14 2 4 0.0000232 20 0.0000232 0.0000232
15 2 8 0.0000232 3 0.0000232 0.0000232
attr(,"effect")$Europe$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 -0.0000319 1 -0.0000319 -0.0000319
2 0 1.94 -0.00000854 7 -0.00000854 -0.00000854
3 0 2.46 0.00000364 7 0.00000364 0.00000364
4 0 2.79 0.00000366 7 0.00000366 0.00000366
5 0 3.19 0.00000366 7 0.00000366 0.00000366
6 0 3.44 0.00000366 7 0.00000366 0.00000366
7 0 3.52 0.00000366 7 0.00000366 0.00000366
8 0 3.73 0.00000366 7 0.00000366 0.00000366
9 0 4.05 0.00000366 7 0.00000366 0.00000366
10 0 5.45 0.00000366 7 0.00000366 0.00000366
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2
attr(,"effect")$Europe$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$Europe$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$Europe$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0844 2 -1.57 -1.57
2 0 TRUE Lincoln Continental -0.0844 0 -0.0844 -0.0844
3 0 FALSE Cadillac Fleetwood -0.0844 2 -1.57 -1.57
4 0 TRUE Cadillac Fleetwood -0.0844 0 -0.0844 -0.0844
5 0 FALSE Merc 450SL -0.0844 2 -1.57 -1.57
6 0 TRUE Merc 450SL -0.0844 0 -0.0844 -0.0844
7 0 FALSE Merc 450SLC -0.0844 2 -1.57 -1.57
8 0 TRUE Merc 450SLC -0.0844 0 -0.0844 -0.0844
9 0 FALSE Camaro Z28 -0.0844 2 -1.57 -1.57
10 0 TRUE Camaro Z28 -0.0844 0 -0.0844 -0.0844
# i 182 more rows
attr(,"effect")$Europe$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.00407 30 0.386 0.386
2 0 TRUE three -0.00407 0 0.427 0.427
3 0 FALSE four -0.00407 8 0.386 0.386
4 0 TRUE four -0.177 16 0.254 0.254
5 0 FALSE five -0.00407 0 0.386 0.386
6 0 TRUE five 0.226 10 0.657 0.657
7 1 FALSE three -0.0677 30 -0.0254 -0.0254
8 1 TRUE three -0.0677 0 -0.212 -0.212
9 1 FALSE four -0.187 8 -0.145 -0.145
10 1 TRUE four -0.241 16 -0.385 -0.385
11 1 FALSE five -0.187 0 -0.145 -0.145
12 1 TRUE five -0.0395 10 -0.183 -0.183
13 2 FALSE three -0.0677 30 -0.500 -0.500
14 2 TRUE three -0.0677 0 -0.355 -0.355
15 2 FALSE four -0.176 8 -0.608 -0.608
16 2 TRUE four -0.175 16 -0.463 -0.463
17 2 FALSE five -0.176 0 -0.608 -0.608
18 2 TRUE five -0.0753 10 -0.363 -0.363
attr(,"effect")$Europe$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0166 6 -0.144 -0.144
2 0 TRUE 1 -0.0166 8 -0.0129 -0.0129
3 0 FALSE 2 -0.0166 12 -0.226 -0.226
4 0 TRUE 2 0.00350 7 0.00350 0.00350
5 0 FALSE 3 -0.0166 7 -0.140 -0.140
6 0 TRUE 3 0.00342 2 -0.0793 -0.0793
7 0 FALSE 4 -0.0166 11 -0.226 -0.226
8 0 TRUE 4 -0.550 5 -0.546 -0.546
9 0 FALSE 8 -0.0166 2 -0.140 -0.140
10 0 TRUE 8 -0.550 4 -0.633 -0.633
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 -0.0139 0 -0.0310 -0.0310
2 0 TRUE 1.50 -0.0139 1 -0.0138 -0.0138
3 0 FALSE 1.94 -0.0139 0 -0.0412 -0.0412
4 0 TRUE 1.94 -0.0138 7 -0.0350 -0.0350
5 0 FALSE 2.46 -0.0139 1 -0.0522 -0.0522
6 0 TRUE 2.46 -0.0138 6 -0.0350 -0.0350
7 0 FALSE 2.79 -0.0139 1 -0.0522 -0.0522
8 0 TRUE 2.79 -0.0378 6 -0.0378 -0.0378
9 0 FALSE 3.19 -0.0139 3 -0.0309 -0.0309
10 0 TRUE 3.19 -0.0761 4 -0.0973 -0.0973
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three 0.301 2 0.301 0.301
2 0 Cadillac Fleetwood three 0.301 2 0.301 0.301
3 0 Merc 450SL three 0.301 2 0.301 0.301
4 0 Merc 450SLC three 0.301 2 0.301 0.301
5 0 Camaro Z28 three 0.301 2 0.301 0.301
6 0 Chrysler Imperial three 0.301 2 0.759 0.759
7 0 Duster 360 three 0.301 2 0.729 0.729
8 0 Merc 450SE three 0.301 2 0.235 0.235
9 0 Hornet Sportabout three 0.301 2 0.794 0.794
10 0 AMC Javelin three 0.301 2 0.964 0.964
# i 278 more rows
attr(,"effect")$Europe$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 -3.89 0 -3.89 -3.89
2 0 Cadillac Fleetwood 1 -3.89 0 -3.89 -3.89
3 0 Merc 450SL 1 -3.89 0 -3.89 -3.89
4 0 Merc 450SLC 1 -3.89 0 -3.89 -3.89
5 0 Camaro Z28 1 -3.89 0 -3.89 -3.89
6 0 Chrysler Imperial 1 -3.89 0 -5.45 -5.45
7 0 Duster 360 1 -3.89 0 -8.67 -8.67
8 0 Merc 450SE 1 -3.89 0 -11.1 -11.1
9 0 Hornet Sportabout 1 -3.89 0 -11.1 -11.1
10 0 AMC Javelin 1 -3.89 0 -11.4 -11.4
# i 470 more rows
attr(,"effect")$Europe$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 -0.403 0 -0.403 -0.403
2 0 Cadillac Fleetwood 1.50 -0.403 0 -0.403 -0.403
3 0 Merc 450SL 1.50 -0.403 0 -0.403 -0.403
4 0 Merc 450SLC 1.50 -0.403 0 -0.403 -0.403
5 0 Camaro Z28 1.50 -0.403 0 -0.403 -0.403
6 0 Chrysler Imperial 1.50 -0.403 0 -0.460 -0.460
7 0 Duster 360 1.50 -0.403 0 -0.555 -0.555
8 0 Merc 450SE 1.50 -0.403 0 -0.778 -0.778
9 0 Hornet Sportabout 1.50 -0.403 0 -0.789 -0.789
10 0 AMC Javelin 1.50 -0.403 0 -0.797 -0.797
# i 950 more rows
attr(,"effect")$Europe$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.00218 6 -0.105 -0.105
2 0 four 1 0.00218 8 -0.155 -0.155
3 0 five 1 0.00218 0 -0.158 -0.158
4 0 three 2 0.00218 8 -0.210 -0.210
5 0 four 2 0.00237 7 -0.210 -0.210
6 0 five 2 0.0348 4 -0.0727 -0.0727
7 0 three 3 0.00218 7 -0.155 -0.155
8 0 four 3 0.00237 2 -0.158 -0.158
9 0 five 3 0.0510 0 -0.161 -0.161
10 0 three 4 0.00218 8 -0.210 -0.210
# i 35 more rows
attr(,"effect")$Europe$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.00213 0 -0.0423 -0.0423
2 0 four 1.50 -0.00213 0 -0.0517 -0.0517
3 0 five 1.50 -0.00213 1 -0.0584 -0.0584
4 0 three 1.94 -0.00213 0 -0.0705 -0.0705
5 0 four 1.94 -0.00210 6 -0.0927 -0.0927
6 0 five 1.94 -0.00282 1 -0.0935 -0.0935
7 0 three 2.46 -0.00213 1 -0.0928 -0.0928
8 0 four 2.46 -0.00208 4 -0.0927 -0.0927
9 0 five 2.46 -0.0208 2 -0.111 -0.111
10 0 three 2.79 -0.00213 1 -0.0928 -0.0928
# i 80 more rows
attr(,"effect")$Europe$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 -0.00454 0 -0.0278 -0.0278
2 0 2 1.50 -0.00454 1 -0.0377 -0.0377
3 0 3 1.50 -0.00454 0 -0.0420 -0.0420
4 0 4 1.50 -0.00454 0 -0.00941 -0.00941
5 0 8 1.50 -0.00454 0 -0.0426 -0.0426
6 0 1 1.94 -0.00454 4 -0.0361 -0.0361
7 0 2 1.94 -0.0244 3 -0.0659 -0.0659
8 0 3 1.94 -0.0244 0 -0.0659 -0.0659
9 0 4 1.94 -0.0244 0 -0.0327 -0.0327
10 0 8 1.94 -0.0244 0 -0.0659 -0.0659
# i 140 more rows
attr(,"effect")$`North America`
attr(,"effect")$`North America`$ale
attr(,"effect")$`North America`$ale$d1
attr(,"effect")$`North America`$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$`North America`$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.72e-11 7.00e-10
attr(,"effect")$`North America`$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
attr(,"effect")$`North America`$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
attr(,"effect")$`North America`$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
attr(,"effect")$`North America`$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.50e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.29e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.09e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
attr(,"effect")$`North America`$ale$d2
attr(,"effect")$`North America`$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$`North America`$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$`North America`$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
2 TRUE Lincoln Continental 0 -0.0218 -1.43 -0.0218 0.0596 1.32
3 FALSE Cadillac Fleetwood 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
4 TRUE Cadillac Fleetwood 0 -0.0218 -1.43 -0.0218 0.0596 1.32
5 FALSE Merc 450SL 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
6 TRUE Merc 450SL 0 -0.0226 -1.43 -0.0226 0.0596 1.31
7 FALSE Merc 450SLC 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
8 TRUE Merc 450SLC 0 -0.0226 -1.43 -0.0226 0.0596 1.31
9 FALSE Camaro Z28 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
10 TRUE Camaro Z28 0 -0.0226 -1.43 -0.0226 0.0596 1.31
# i 54 more rows
attr(,"effect")$`North America`$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 0.139 -0.101 0.139 0.205 0.323
2 TRUE three 0 0.139 -0.605 0.139 0.170 0.858
3 FALSE four 8 0.313 0.153 0.313 0.205 0.566
4 TRUE four 16 0.631 -0.263 0.631 0.746 1.43
5 FALSE five 0 0.313 0.153 0.313 0.205 0.566
6 TRUE five 10 0.728 -0.259 0.728 0.843 1.62
attr(,"effect")$`North America`$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0000000460 -0.00351 -4.60e-8 2.63e-8 3.51e-3
2 TRUE 1 8 -0.0000000460 -0.117 -4.60e-8 2.47e-7 1.17e-1
3 FALSE 2 12 -0.000000106 -0.0786 -1.06e-7 2.31e-8 7.86e-2
4 TRUE 2 7 -0.000000373 -0.121 -3.73e-7 -5.45e-8 1.21e-1
5 FALSE 3 7 -0.000000106 -0.00351 -1.06e-7 2.19e-8 3.50e-3
6 TRUE 3 2 -0.000000373 -0.199 -3.73e-7 1.90e-8 1.99e-1
7 FALSE 4 11 -0.000000109 -0.000000220 -1.09e-7 -5.33e-8 -4.64e-8
8 TRUE 4 5 -0.000000380 -0.117 -3.80e-7 1.25e-8 1.17e-1
9 FALSE 8 2 -0.000000110 -0.0786 -1.10e-7 1.95e-8 7.86e-2
10 TRUE 8 4 -0.000000380 -0.199 -3.80e-7 1.30e-8 1.99e-1
attr(,"effect")$`North America`$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 9.93e-11 -1.11e- 5 9.93e-11 4.34e-10 0.0000111
2 TRUE 1.50 1 9.93e-11 -1.62e- 2 9.93e-11 5.51e-10 0.0162
3 FALSE 1.94 0 9.85e-11 -2.01e- 2 9.85e-11 -1.47e-10 0.0201
4 TRUE 1.94 7 -4.37e-10 -3.64e- 2 -4.37e-10 -5.09e-11 0.0364
5 FALSE 2.46 1 7.33e-11 -2.01e- 2 7.33e-11 -1.75e-10 0.0201
6 TRUE 2.46 6 -5.10e-10 -3.64e- 2 -5.10e-10 -7.93e-11 0.0364
7 FALSE 2.79 1 1.10e- 9 1.67e-10 1.10e- 9 1.45e- 9 0.00000000174
8 TRUE 2.79 6 1.32e- 9 -1.62e- 2 1.32e- 9 3.23e- 9 0.0162
9 FALSE 3.19 3 1.34e- 9 -9.74e- 3 1.34e- 9 1.51e- 9 0.00974
10 TRUE 3.19 4 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
11 FALSE 3.44 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
12 TRUE 3.44 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
13 FALSE 3.52 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
14 TRUE 3.52 0 1.56e- 9 -1.62e- 2 1.56e- 9 2.90e- 9 0.0162
15 FALSE 3.73 5 1.34e- 9 -1.69e- 5 1.34e- 9 2.11e- 9 0.0000169
16 TRUE 3.73 2 1.56e- 9 -2.60e- 2 1.56e- 9 2.66e- 9 0.0260
17 FALSE 4.05 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
18 TRUE 4.05 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
19 FALSE 5.45 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
20 TRUE 5.45 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
attr(,"effect")$`North America`$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
attr(,"effect")$`North America`$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
attr(,"effect")$`North America`$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
attr(,"effect")$`North America`$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
attr(,"effect")$`North America`$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
attr(,"effect")$`North America`$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
attr(,"effect")$`North America`$stats
attr(,"effect")$`North America`$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$`North America`$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$`North America`$boot_data
attr(,"effect")$`North America`$boot_data$d1
attr(,"effect")$`North America`$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$`North America`$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.52e-10 38 1.52e-10 1.52e-10
2 0 TRUE -2.23e-10 26 -2.23e-10 -2.23e-10
3 1 FALSE -9.71e-10 38 -9.71e-10 -9.71e-10
4 1 TRUE 7.39e-10 26 7.39e-10 7.39e-10
5 2 FALSE -8.52e-10 38 -8.52e-10 -8.52e-10
6 2 TRUE -4.72e-11 26 -4.72e-11 -4.72e-11
attr(,"effect")$`North America`$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.139 2 0.139 0.139
2 0 Cadillac Fleetwood 0.139 2 0.139 0.139
3 0 Merc 450SL -0.861 2 -0.861 -0.861
4 0 Merc 450SLC -0.861 2 -0.861 -0.861
5 0 Camaro Z28 0.139 2 0.139 0.139
6 0 Chrysler Imperial 0.139 2 0.139 0.139
7 0 Duster 360 0.139 2 0.139 0.139
8 0 Merc 450SE -0.861 2 -0.861 -0.861
9 0 Hornet Sportabout 0.139 2 0.139 0.139
10 0 AMC Javelin 0.139 2 0.139 0.139
# i 86 more rows
attr(,"effect")$`North America`$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.254 30 0.254 0.254
2 0 four -0.283 24 -0.283 -0.283
3 0 five -0.0843 10 -0.0843 -0.0843
4 1 three 0.0526 30 0.0526 0.0526
5 1 four -0.232 24 -0.232 -0.232
6 1 five -0.133 10 -0.133 -0.133
7 2 three 0.0544 30 0.0544 0.0544
8 2 four -0.232 24 -0.232 -0.232
9 2 five 0.0679 10 0.0679 0.0679
attr(,"effect")$`North America`$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.08e-7 14 1.08e-7 1.08e-7
2 0 2 -3.71e-8 19 -3.71e-8 -3.71e-8
3 0 3 -3.71e-8 9 -3.71e-8 -3.71e-8
4 0 4 -3.71e-8 16 -3.71e-8 -3.71e-8
5 0 8 -4.13e-8 6 -4.13e-8 -4.13e-8
6 1 1 1.08e-7 18 1.08e-7 1.08e-7
7 1 2 -7.21e-8 16 -7.21e-8 -7.21e-8
8 1 3 -7.21e-8 13 -7.21e-8 -7.21e-8
9 1 4 -7.21e-8 15 -7.21e-8 -7.21e-8
10 1 8 -8.38e-8 2 -8.38e-8 -8.38e-8
11 2 1 1.08e-7 15 1.08e-7 1.08e-7
12 2 2 1.40e-9 13 1.40e-9 1.40e-9
13 2 3 1.40e-9 13 1.40e-9 1.40e-9
14 2 4 1.40e-9 20 1.40e-9 1.40e-9
15 2 8 1.17e-9 3 1.17e-9 1.17e-9
attr(,"effect")$`North America`$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 6.60e-10 1 6.60e-10 6.60e-10
2 0 1.94 -8.93e-12 7 -8.93e-12 -8.93e-12
3 0 2.46 -4.95e-11 7 -4.95e-11 -4.95e-11
4 0 2.79 -4.95e-11 7 -4.95e-11 -4.95e-11
5 0 3.19 -4.95e-11 7 -4.95e-11 -4.95e-11
6 0 3.44 -4.87e-11 7 -4.87e-11 -4.87e-11
7 0 3.52 -4.87e-11 7 -4.87e-11 -4.87e-11
8 0 3.73 -4.87e-11 7 -4.87e-11 -4.87e-11
9 0 4.05 -4.87e-11 7 -4.87e-11 -4.87e-11
10 0 5.45 -4.87e-11 7 -4.87e-11 -4.87e-11
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2
attr(,"effect")$`North America`$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$`North America`$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$`North America`$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0218 2 -0.0218 -0.0218
2 0 TRUE Lincoln Continental -0.0218 0 -1.51 -1.51
3 0 FALSE Cadillac Fleetwood -0.0218 2 -0.0218 -0.0218
4 0 TRUE Cadillac Fleetwood -0.0218 0 -1.51 -1.51
5 0 FALSE Merc 450SL -0.0218 2 -0.0218 -0.0218
6 0 TRUE Merc 450SL -0.0218 0 -1.51 -1.51
7 0 FALSE Merc 450SLC -0.0218 2 -0.0218 -0.0218
8 0 TRUE Merc 450SLC -0.0218 0 -1.51 -1.51
9 0 FALSE Camaro Z28 -0.0218 2 -0.0218 -0.0218
10 0 TRUE Camaro Z28 -0.0218 0 -1.51 -1.51
# i 182 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0.0729 30 0.205 0.205
2 0 TRUE three 0.0729 0 0.894 0.894
3 0 FALSE four 0.0729 8 0.205 0.205
4 0 TRUE four 0.643 16 1.46 1.46
5 0 FALSE five 0.0729 0 0.205 0.205
6 0 TRUE five 0.838 10 1.66 1.66
7 1 FALSE three 0.271 30 0.329 0.329
8 1 TRUE three 0.271 0 0.170 0.170
9 1 FALSE four 0.527 8 0.585 0.585
10 1 TRUE four 0.848 16 0.746 0.746
11 1 FALSE five 0.527 0 0.585 0.585
12 1 TRUE five 0.945 10 0.843 0.843
13 2 FALSE three 0.0732 30 -0.117 -0.117
14 2 TRUE three 0.0732 0 -0.646 -0.646
15 2 FALSE four 0.340 8 0.150 0.150
16 2 TRUE four 0.403 16 -0.317 -0.317
17 2 FALSE five 0.340 0 0.150 0.150
18 2 TRUE five 0.403 10 -0.317 -0.317
attr(,"effect")$`North America`$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0000000460 6 0.00370 0.00370
2 0 TRUE 1 -0.0000000460 8 -0.123 -0.123
3 0 FALSE 2 -0.0000000460 12 -0.0828 -0.0828
4 0 TRUE 2 -0.000000439 7 -0.127 -0.127
5 0 FALSE 3 -0.0000000460 7 0.00369 0.00369
6 0 TRUE 3 -0.000000439 2 -0.210 -0.210
7 0 FALSE 4 -0.0000000460 11 -0.0000000460 -0.0000000460
8 0 TRUE 4 -0.000000446 5 -0.123 -0.123
9 0 FALSE 8 -0.0000000460 2 -0.0828 -0.0828
10 0 TRUE 8 -0.000000447 4 -0.210 -0.210
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 9.93e-11 0 1.17e- 5 1.17e- 5
2 0 TRUE 1.50 9.93e-11 1 -1.71e- 2 -1.71e- 2
3 0 FALSE 1.94 9.93e-11 0 -2.12e- 2 -2.12e- 2
4 0 TRUE 1.94 -5.71e-10 7 -3.83e- 2 -3.83e- 2
5 0 FALSE 2.46 9.93e-11 1 -2.12e- 2 -2.12e- 2
6 0 TRUE 2.46 -6.18e-10 6 -3.83e- 2 -3.83e- 2
7 0 FALSE 2.79 9.93e-11 1 9.93e-11 9.93e-11
8 0 TRUE 2.79 7.83e-10 6 -1.71e- 2 -1.71e- 2
9 0 FALSE 3.19 9.93e-11 3 -1.03e- 2 -1.03e- 2
10 0 TRUE 3.19 7.86e-10 4 -3.83e- 2 -3.83e- 2
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.117 2 -0.117 -0.117
2 0 Cadillac Fleetwood three -0.117 2 -0.117 -0.117
3 0 Merc 450SL three -0.117 2 -0.117 -0.117
4 0 Merc 450SLC three -0.117 2 -0.117 -0.117
5 0 Camaro Z28 three -0.117 2 -0.117 -0.117
6 0 Chrysler Imperial three -0.117 2 0.339 0.339
7 0 Duster 360 three -0.117 2 0.317 0.317
8 0 Merc 450SE three -0.117 2 0.317 0.317
9 0 Hornet Sportabout three -0.117 2 0.434 0.434
10 0 AMC Javelin three -0.117 2 0.547 0.547
# i 278 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 0.471 0 0.471 0.471
2 0 Cadillac Fleetwood 1 0.471 0 0.471 0.471
3 0 Merc 450SL 1 0.471 0 0.471 0.471
4 0 Merc 450SLC 1 0.471 0 0.471 0.471
5 0 Camaro Z28 1 0.471 0 0.471 0.471
6 0 Chrysler Imperial 1 0.471 0 -1.53 -1.53
7 0 Duster 360 1 0.471 0 -5.55 -5.55
8 0 Merc 450SE 1 0.471 0 -6.69 -6.69
9 0 Hornet Sportabout 1 0.471 0 -6.97 -6.97
10 0 AMC Javelin 1 0.471 0 -7.07 -7.07
# i 470 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.0603 0 0.0603 0.0603
2 0 Cadillac Fleetwood 1.50 0.0603 0 0.0603 0.0603
3 0 Merc 450SL 1.50 0.0603 0 0.0603 0.0603
4 0 Merc 450SLC 1.50 0.0603 0 0.0603 0.0603
5 0 Camaro Z28 1.50 0.0603 0 0.0602 0.0602
6 0 Chrysler Imperial 1.50 0.0603 0 0.00250 0.00250
7 0 Duster 360 1.50 0.0603 0 -0.252 -0.252
8 0 Merc 450SE 1.50 0.0603 0 -0.318 -0.318
9 0 Hornet Sportabout 1.50 0.0603 0 -0.334 -0.334
10 0 AMC Javelin 1.50 0.0603 0 -0.334 -0.334
# i 950 more rows
attr(,"effect")$`North America`$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 -0.0306 6 -0.108 -0.108
2 0 four 1 -0.0306 8 -0.157 -0.157
3 0 five 1 -0.0306 0 -0.160 -0.160
4 0 three 2 -0.0306 8 -0.213 -0.213
5 0 four 2 -0.165 7 -0.347 -0.347
6 0 five 2 -0.165 4 -0.242 -0.242
7 0 three 3 -0.0306 7 -0.157 -0.157
8 0 four 3 -0.195 2 -0.325 -0.325
9 0 five 3 -0.198 0 -0.380 -0.380
10 0 three 4 -0.0306 8 -0.213 -0.213
# i 35 more rows
attr(,"effect")$`North America`$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.0342 0 -0.0903 -0.0903
2 0 four 1.50 -0.0342 0 -0.0997 -0.0997
3 0 five 1.50 -0.0342 1 -0.106 -0.106
4 0 three 1.94 -0.0342 0 -0.119 -0.119
5 0 four 1.94 -0.0506 6 -0.157 -0.157
6 0 five 1.94 -0.0506 1 -0.157 -0.157
7 0 three 2.46 -0.0342 1 -0.141 -0.141
8 0 four 2.46 -0.0578 4 -0.164 -0.164
9 0 five 2.46 -0.0578 2 -0.164 -0.164
10 0 three 2.79 -0.0342 1 -0.141 -0.141
# i 80 more rows
attr(,"effect")$`North America`$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.0000000171 0 -0.0332 -0.0332
2 0 2 1.50 0.0000000171 1 -0.00425 -0.00425
3 0 3 1.50 0.0000000171 0 -0.0375 -0.0375
4 0 4 1.50 0.0000000171 0 -0.0281 -0.0281
5 0 8 1.50 0.0000000171 0 -0.0381 -0.0381
6 0 1 1.94 0.0000000171 4 -0.0415 -0.0415
7 0 2 1.94 0.000000145 3 -0.00831 -0.00831
8 0 3 1.94 0.000000145 0 -0.0415 -0.0415
9 0 4 1.94 0.000000145 0 -0.0316 -0.0316
10 0 8 1.94 0.000000145 0 -0.0415 -0.0415
# i 140 more rows
attr(,"params")
attr(,"params")$max_d
[1] 2
attr(,"params")$ordered_x_cols
attr(,"params")$ordered_x_cols$d1
[1] "mpg" "vs" "am" "model" "gear" "carb" "wt"
attr(,"params")$ordered_x_cols$d2
[1] "mpg:vs" "mpg:am" "mpg:model" "mpg:gear" "mpg:carb"
[6] "mpg:wt" "vs:am" "vs:model" "vs:gear" "vs:carb"
[11] "vs:wt" "am:model" "am:gear" "am:carb" "am:wt"
[16] "model:gear" "model:carb" "model:wt" "gear:carb" "gear:wt"
[21] "carb:wt"
attr(,"params")$requested_x_cols
attr(,"params")$requested_x_cols$d1
[1] "mpg" "vs" "am" "model" "gear" "carb" "wt"
attr(,"params")$requested_x_cols$d2
[1] "mpg:vs" "mpg:am" "mpg:model" "mpg:gear" "mpg:carb"
[6] "mpg:wt" "vs:am" "vs:model" "vs:gear" "vs:carb"
[11] "vs:wt" "am:model" "am:gear" "am:carb" "am:wt"
[16] "model:gear" "model:carb" "model:wt" "gear:carb" "gear:wt"
[21] "carb:wt"
attr(,"params")$y_cats
[1] "Asia" "Europe" "North America"
attr(,"params")$y_summary
continent Asia Europe North America
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
1% 1.069668e-33 2.992722e-30 1.069668e-33 2.347822e-34
2.5% 6.673430e-30 4.423735e-28 1.517465e-32 6.673430e-30
5% 3.090197e-29 3.706601e-27 1.024412e-30 3.090197e-29
10% 7.497546e-28 7.833412e-24 7.497546e-28 8.934872e-29
20% 4.002630e-25 6.716097e-21 4.002630e-25 4.655065e-26
25% 3.199266e-24 8.785222e-20 8.693125e-25 3.199266e-24
30% 6.827727e-23 2.256763e-18 6.827727e-23 2.889855e-23
40% 7.271081e-18 8.144755e-16 5.332080e-18 7.271081e-18
aler_lo_lo 1.197313e-14 1.197313e-14 1.305088e-14 2.637676e-14
aler_lo 1.197341e-14 1.197341e-14 1.305116e-14 2.637704e-14
50% 1.305186e-14 1.197411e-14 1.305186e-14 2.637774e-14
mean 3.333333e-01 3.333333e-01 3.333333e-01 3.333333e-01
aler_hi 2.637802e-14 1.197440e-14 1.305215e-14 2.637802e-14
aler_hi_hi 2.637866e-14 1.197504e-14 1.305278e-14 2.637866e-14
60% 1.134760e-08 9.938550e-13 9.999865e-01 1.134760e-08
70% 1.000000e+00 5.030736e-08 1.000000e+00 1.000000e+00
75% 1.000000e+00 4.755457e-06 1.000000e+00 1.000000e+00
80% 1.000000e+00 1.373114e-05 1.000000e+00 1.000000e+00
90% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
95% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
97.5% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
99% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
max 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
attr(,"params")$model
attr(,"params")$model$class
[1] "multinom" "nnet"
attr(,"params")$model$call
[1] "nnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)"
attr(,"params")$model$print
[1] "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)\n\nCoefficients:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope -21.86205 -2.222771 -6.526356\nNorth America -12.29302 -2.006450 14.817906\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope -3.5495185 -42.28844 -3.766810\nNorth America -0.4119232 -22.35666 7.689393\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope -7.046867 5.545836 42.144872 43.426334\nNorth America 20.851301 -3.241914 2.522922 2.870462\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope -23.11127 -27.5878163 -2.495884\nNorth America 29.30682 -0.1513286 7.771946\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope -5.036042 -1.780289 14.561696\nNorth America 12.103127 -2.891703 -1.631007\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 6.999218 -33.61112 -35.88135\nNorth America -7.857123 -3.27005 -10.72975\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 14.2684900 14.2452958 11.590028 10.4496565\nNorth America -0.5204999 -0.6708756 1.228568 0.8928993\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 39.49499 34.5616 36.09627\nNorth America -38.59611 -31.8537 -34.17067\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope -3.057543 15.344405 -34.531039\nNorth America 4.399831 -4.102117 -8.804816\n modelToyota Corona modelValiant modelVolvo 142E wt\nEurope -44.72605 -2.073514 34.932615 4.534617\nNorth America -52.77315 5.537827 2.626442 3.625818\n amTRUE gear.L gear.Q carb\nEurope -34.05607 40.7995829 -5.443825 10.524876\nNorth America -24.81811 0.6220113 39.516400 5.894396\n\nResidual Deviance: 0.0001161393 \nAIC: 136.0001 "
attr(,"params")$model$summary
[1] "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)\n\nCoefficients:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope -21.86205 -2.222771 -6.526356\nNorth America -12.29302 -2.006450 14.817906\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope -3.5495185 -42.28844 -3.766810\nNorth America -0.4119232 -22.35666 7.689393\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope -7.046867 5.545836 42.144872 43.426334\nNorth America 20.851301 -3.241914 2.522922 2.870462\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope -23.11127 -27.5878163 -2.495884\nNorth America 29.30682 -0.1513286 7.771946\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope -5.036042 -1.780289 14.561696\nNorth America 12.103127 -2.891703 -1.631007\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 6.999218 -33.61112 -35.88135\nNorth America -7.857123 -3.27005 -10.72975\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 14.2684900 14.2452958 11.590028 10.4496565\nNorth America -0.5204999 -0.6708756 1.228568 0.8928993\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 39.49499 34.5616 36.09627\nNorth America -38.59611 -31.8537 -34.17067\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope -3.057543 15.344405 -34.531039\nNorth America 4.399831 -4.102117 -8.804816\n modelToyota Corona modelValiant modelVolvo 142E wt\nEurope -44.72605 -2.073514 34.932615 4.534617\nNorth America -52.77315 5.537827 2.626442 3.625818\n amTRUE gear.L gear.Q carb\nEurope -34.05607 40.7995829 -5.443825 10.524876\nNorth America -24.81811 0.6220113 39.516400 5.894396\n\nStd. Errors:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope 35852.83 64.65729 NaN\nNorth America 12968.78 64.70730 2.870067e-08\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope 477.3874 1.590757e-08 2.858243e-08\nNorth America 477.3874 2.200012e-18 6.428719e+00\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope 5.486580e-10 2660.898 1.805399e+04 1.525317e+04\nNorth America 7.249665e-11 2660.898 1.192455e-12 4.867466e-13\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope 0.09603878 5.871595e-07 3.863891e-10\nNorth America 0.09624948 9.331882e-08 7.163294e+03\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope 4.655689e-09 285.0759 32194.07\nNorth America 5.351209e-02 285.1436 20099.45\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 0.01739745 644.72277405 1.000913e+02\nNorth America 0.01739745 0.04282998 3.394594e-05\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 1.329222e+02 1.088958e+02 4.023889e-07 2.701645e-06\nNorth America 4.475756e-08 3.800080e-08 2.855911e-10 1.280090e-09\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 92448.24826 22918.50 1179.3388\nNorth America 12.05893 21677.09 776.0876\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope 1.504060e-06 29024.38 9.393802e-15\nNorth America 6.422677e+01 62881.87 2.008891e-13\n modelToyota Corona modelValiant modelVolvo 142E wt amTRUE\nEurope 2.042457e-17 7.332842e-09 5.751337e+04 26852.73 81773.79\nNorth America 1.689525e+01 3.329289e+04 5.749185e-12 97682.05 57698.40\n gear.L gear.Q carb\nEurope 120502.95 45794.583 77388.60\nNorth America 84695.32 5294.488 34719.66\n\nResidual Deviance: 0.0001161393 \nAIC: 136.0001 "
attr(,"params")$data
attr(,"params")$data$data_sample
# A tibble: 64 x 8
mpg vs continent am model gear carb wt
<dbl> <lgl> <fct> <lgl> <chr> <ord> <int> <dbl>
1 21 FALSE Asia TRUE Mazda RX4 four 4 2.62
2 21 FALSE Asia TRUE Mazda RX4 Wag four 4 2.88
3 22.8 TRUE Asia TRUE Datsun 710 four 1 2.32
4 21.4 TRUE North America FALSE Hornet 4 Drive three 1 3.22
5 18.7 FALSE North America FALSE Hornet Sportabout three 2 3.44
6 18.1 TRUE North America FALSE Valiant three 1 3.46
7 14.3 FALSE North America FALSE Duster 360 three 4 3.57
8 24.4 TRUE Europe FALSE Merc 240D four 2 3.19
9 22.8 TRUE Europe FALSE Merc 230 four 2 3.15
10 19.2 TRUE Europe FALSE Merc 280 four 4 3.44
# i 54 more rows
attr(,"params")$data$y_vals_sample
Asia Europe North America
1 1.000000e+00 3.101887e-14 6.537610e-18
2 1.000000e+00 1.018274e-14 9.489785e-21
3 1.000000e+00 2.627972e-32 2.366458e-34
4 3.355506e-13 2.265633e-26 1.000000e+00
5 5.377066e-18 2.956660e-27 1.000000e+00
6 1.288929e-12 4.032610e-25 1.000000e+00
7 4.043404e-27 7.439102e-28 1.000000e+00
8 8.984341e-15 1.000000e+00 2.843402e-24
9 1.052416e-14 1.000000e+00 3.348575e-24
10 2.968411e-23 1.000000e+00 2.047714e-26
11 9.284979e-23 1.000000e+00 4.578751e-26
12 1.284023e-16 1.000000e+00 8.157836e-18
13 8.331004e-14 1.000000e+00 1.307688e-12
14 1.431293e-14 1.000000e+00 2.654720e-14
15 7.731999e-20 3.603019e-15 1.000000e+00
16 9.971464e-20 1.592098e-14 1.000000e+00
17 1.112253e-20 2.115769e-16 1.000000e+00
18 1.405315e-05 9.999859e-01 1.373965e-28
19 1.000000e+00 9.694352e-23 2.934333e-23
20 1.000000e+00 6.815207e-30 3.132362e-29
21 1.000000e+00 1.028218e-33 9.973236e-16
22 3.322245e-16 9.342366e-25 1.000000e+00
23 9.879611e-13 8.170982e-20 1.000000e+00
24 6.336585e-25 6.674374e-25 1.000000e+00
25 2.743479e-15 6.845408e-23 1.000000e+00
26 1.297545e-05 9.999870e-01 6.870224e-29
27 5.019518e-08 9.999999e-01 3.082157e-10
28 1.885279e-06 9.999981e-01 1.410710e-08
29 9.136296e-20 5.361045e-18 1.000000e+00
30 2.703016e-23 1.000000e+00 6.687763e-14
31 1.211644e-34 1.000000e+00 7.112017e-21
32 3.688923e-08 1.000000e+00 1.189251e-27
33 1.000000e+00 3.408497e-14 7.049392e-18
34 1.000000e+00 2.572858e-19 2.489552e-23
35 1.000000e+00 2.558207e-32 2.316091e-34
36 3.298994e-13 2.275299e-26 1.000000e+00
37 1.337882e-20 3.110842e-25 1.000000e+00
38 1.389107e-12 3.957660e-25 1.000000e+00
39 3.647166e-27 7.633916e-28 1.000000e+00
40 7.899779e-15 1.000000e+00 2.771028e-24
41 1.005165e-14 1.000000e+00 3.317888e-24
42 7.658342e-28 1.000000e+00 1.980485e-28
43 1.064524e-22 1.000000e+00 4.705941e-26
44 5.328465e-12 1.000000e+00 8.550926e-16
45 2.496435e-18 1.000000e+00 1.303274e-14
46 1.342406e-14 1.000000e+00 2.620827e-14
47 2.933308e-17 3.474335e-17 1.000000e+00
48 3.255127e-17 1.594157e-16 1.000000e+00
49 3.066813e-23 2.169517e-14 1.000000e+00
50 1.345603e-05 9.999865e-01 1.362061e-28
51 1.000000e+00 1.041859e-22 3.108341e-23
52 1.000000e+00 6.680493e-30 3.082756e-29
53 1.000000e+00 1.094012e-33 1.048032e-15
54 2.973320e-16 9.605844e-25 1.000000e+00
55 1.061403e-12 8.025443e-20 1.000000e+00
56 6.074529e-25 6.745405e-25 1.000000e+00
57 3.045398e-15 6.668596e-23 1.000000e+00
58 1.351647e-05 9.999865e-01 6.926697e-29
59 5.131701e-08 9.999999e-01 3.095840e-10
60 2.015459e-06 9.999980e-01 1.429715e-08
61 9.386677e-20 5.324839e-18 1.000000e+00
62 2.463284e-23 1.000000e+00 6.564435e-14
63 4.750282e-30 1.000000e+00 7.370275e-19
64 9.953285e-13 1.000000e+00 1.160579e-29
attr(,"params")$data$nrow
[1] 64
attr(,"params")$y_col
[1] "continent"
attr(,"params")$parallel
[1] 0
attr(,"params")$model_packages
NULL
attr(,"params")$output_stats
[1] TRUE
attr(,"params")$output_boot_data
[1] TRUE
attr(,"params")$pred_fun
[1] "function(object, newdata, type = pred_type) {\n stats::predict(object = object, newdata = newdata, type = type)\n }"
attr(,"params")$pred_type
[1] "probs"
attr(,"params")$p_values
<ale::ALEpDist>
@ rand_stats :List of 3
.. $ Asia : tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. $ Europe : tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. $ North America: tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
@ residual_distribution: 'univariateML' Named num [1:2] -2.04e-23 1.50e-17
.. - attr(*, "logLik")= num 2371
.. - attr(*, "call")= language f(x = x, na.rm = na.rm)
.. - attr(*, "n")= int 64
.. - attr(*, "model")= chr "Laplace"
.. - attr(*, "density")= chr "extraDistr::dlaplace"
.. - attr(*, "support")= num [1:2] -Inf Inf
.. - attr(*, "names")= chr [1:2] "mu" "sigma"
.. - attr(*, "default")= num [1:2] 0 1
.. - attr(*, "continuous")= logi TRUE
@ residuals : NULL
@ params :List of 11
.. $ model :List of 4
.. ..$ class : chr [1:2] "multinom" "nnet"
.. ..$ call : chr "nnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)"
.. ..$ print : chr "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALS"| __truncated__
.. ..$ summary: chr "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALS"| __truncated__
.. $ y_col : chr "continent"
.. $ rand_it : NULL
.. $ parallel : num 0
.. $ model_packages : NULL
.. $ random_model_call_string : NULL
.. $ random_model_call_string_vars: chr(0)
.. $ positive : logi TRUE
.. $ seed : num 0
.. $ rand_it_ok : int 100
.. $ exactness : chr "surrogate"
attr(,"params")$aler_alpha
[1] 0.01 0.05
attr(,"params")$max_num_bins
[1] 10
attr(,"params")$boot_it
[1] 2
attr(,"params")$boot_alpha
[1] 0.05
attr(,"params")$boot_centre
[1] "mean"
attr(,"params")$seed
[1] 0
attr(,"params")$y_type
[1] "categorical"
attr(,"params")$sample_size
[1] 500
Code
get(cat_cars_ale)
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
$Asia$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
$Asia$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
$Asia$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
$Europe$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
$Europe$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
$Europe$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 28 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.71e-11 7.00e-10
$`North America`$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
$`North America`$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
$`North America`$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
$`North America`$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.47e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.35e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.28e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 Lincoln Contine~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 310 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 three 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 40 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 90 more rows
$`North America`$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE FALSE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE TRUE 12 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE TRUE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE Lincoln Continent~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE Cadillac Fleetwood 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE Cadillac Fleetwood 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE Merc 450SL 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE Merc 450SL 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE Merc 450SLC 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE Merc 450SLC 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE Camaro Z28 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE Camaro Z28 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 54 more rows
$`North America`$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE three 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE four 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE four 20 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE five 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE five 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 2 10 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 2 9 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 3 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 3 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 4 13 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 4 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 8 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 8 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 1.94 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 1.94 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 2.46 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 2.46 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 2.79 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 2.79 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 3.19 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 3.19 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 FALSE 3.44 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 TRUE 3.44 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 FALSE 3.52 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 TRUE 3.52 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 FALSE 3.73 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 TRUE 3.73 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 FALSE 4.05 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 TRUE 4.05 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 FALSE 5.45 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 TRUE 5.45 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
2 TRUE Lincoln Continental 0 -0.0218 -1.43 -0.0218 0.0596 1.32
3 FALSE Cadillac Fleetwood 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
4 TRUE Cadillac Fleetwood 0 -0.0218 -1.43 -0.0218 0.0596 1.32
5 FALSE Merc 450SL 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
6 TRUE Merc 450SL 0 -0.0226 -1.43 -0.0226 0.0596 1.31
7 FALSE Merc 450SLC 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
8 TRUE Merc 450SLC 0 -0.0226 -1.43 -0.0226 0.0596 1.31
9 FALSE Camaro Z28 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
10 TRUE Camaro Z28 0 -0.0226 -1.43 -0.0226 0.0596 1.31
# i 54 more rows
$`North America`$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 0.139 -0.101 0.139 0.205 0.323
2 TRUE three 0 0.139 -0.605 0.139 0.170 0.858
3 FALSE four 8 0.313 0.153 0.313 0.205 0.566
4 TRUE four 16 0.631 -0.263 0.631 0.746 1.43
5 FALSE five 0 0.313 0.153 0.313 0.205 0.566
6 TRUE five 10 0.728 -0.259 0.728 0.843 1.62
$`North America`$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0000000460 -0.00351 -4.60e-8 2.63e-8 3.51e-3
2 TRUE 1 8 -0.0000000460 -0.117 -4.60e-8 2.47e-7 1.17e-1
3 FALSE 2 12 -0.000000106 -0.0786 -1.06e-7 2.31e-8 7.86e-2
4 TRUE 2 7 -0.000000373 -0.121 -3.73e-7 -5.45e-8 1.21e-1
5 FALSE 3 7 -0.000000106 -0.00351 -1.06e-7 2.19e-8 3.50e-3
6 TRUE 3 2 -0.000000373 -0.199 -3.73e-7 1.90e-8 1.99e-1
7 FALSE 4 11 -0.000000109 -0.000000220 -1.09e-7 -5.33e-8 -4.64e-8
8 TRUE 4 5 -0.000000380 -0.117 -3.80e-7 1.25e-8 1.17e-1
9 FALSE 8 2 -0.000000110 -0.0786 -1.10e-7 1.95e-8 7.86e-2
10 TRUE 8 4 -0.000000380 -0.199 -3.80e-7 1.30e-8 1.99e-1
$`North America`$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 9.93e-11 -1.11e- 5 9.93e-11 4.34e-10 0.0000111
2 TRUE 1.50 1 9.93e-11 -1.62e- 2 9.93e-11 5.51e-10 0.0162
3 FALSE 1.94 0 9.85e-11 -2.01e- 2 9.85e-11 -1.47e-10 0.0201
4 TRUE 1.94 7 -4.37e-10 -3.64e- 2 -4.37e-10 -5.09e-11 0.0364
5 FALSE 2.46 1 7.33e-11 -2.01e- 2 7.33e-11 -1.75e-10 0.0201
6 TRUE 2.46 6 -5.10e-10 -3.64e- 2 -5.10e-10 -7.92e-11 0.0364
7 FALSE 2.79 1 1.10e- 9 1.67e-10 1.10e- 9 1.45e- 9 0.00000000174
8 TRUE 2.79 6 1.32e- 9 -1.62e- 2 1.32e- 9 3.24e- 9 0.0162
9 FALSE 3.19 3 1.34e- 9 -9.74e- 3 1.34e- 9 1.51e- 9 0.00974
10 TRUE 3.19 4 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
11 FALSE 3.44 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
12 TRUE 3.44 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
13 FALSE 3.52 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
14 TRUE 3.52 0 1.56e- 9 -1.62e- 2 1.56e- 9 2.90e- 9 0.0162
15 FALSE 3.73 5 1.34e- 9 -1.69e- 5 1.34e- 9 2.11e- 9 0.0000169
16 TRUE 3.73 2 1.56e- 9 -2.60e- 2 1.56e- 9 2.66e- 9 0.0260
17 FALSE 4.05 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
18 TRUE 4.05 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
19 FALSE 5.45 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
20 TRUE 5.45 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
$`North America`$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
$`North America`$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
$`North America`$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
$`North America`$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
$`North America`$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
$`North America`$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
Code
get(cat_cars_ale, what = "boot_data")
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 1.20e-14 7 1.20e-14 1.20e-14
4 0 16.4 1.20e-14 7 1.20e-14 1.20e-14
5 0 18.7 1.20e-14 7 1.20e-14 1.20e-14
6 0 19.8 1.20e-14 7 1.20e-14 1.20e-14
7 0 21.4 1.20e-14 7 1.20e-14 1.20e-14
8 0 22.9 1.20e-14 7 1.20e-14 1.20e-14
9 0 30.1 1.20e-14 7 1.20e-14 1.20e-14
10 0 33.9 1.20e-14 7 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.20e-14 36 1.20e-14 1.20e-14
2 0 TRUE 1.20e-14 28 1.20e-14 1.20e-14
3 1 FALSE 1.20e-14 36 1.20e-14 1.20e-14
4 1 TRUE 1.20e-14 28 1.20e-14 1.20e-14
5 2 FALSE 1.20e-14 36 1.20e-14 1.20e-14
6 2 TRUE 1.20e-14 28 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE -0.0432 38 -0.0432 -0.0432
2 0 TRUE 0.0632 26 0.0632 0.0632
3 1 FALSE 0.0321 38 0.0321 0.0321
4 1 TRUE -0.0101 26 -0.0101 -0.0101
5 2 FALSE 0.0297 38 0.0297 0.0297
6 2 TRUE -0.00670 26 -0.00670 -0.00670
$Asia$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental -0.305 2 -0.305 -0.305
2 0 Cadillac Fleetwood -0.305 2 -0.305 -0.305
3 0 Merc 450SL -0.305 2 -0.305 -0.305
4 0 Merc 450SLC -0.305 2 -0.305 -0.305
5 0 Camaro Z28 -0.305 2 -0.305 -0.305
6 0 Chrysler Imperial -0.305 2 -0.305 -0.305
7 0 Duster 360 -0.305 2 -0.305 -0.305
8 0 Merc 450SE -0.305 2 -0.305 -0.305
9 0 Hornet Sportabout -0.305 2 -0.305 -0.305
10 0 AMC Javelin -0.305 2 -0.305 -0.305
# i 86 more rows
$Asia$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.118 30 0.118 0.118
2 0 four 0.0718 24 0.0718 0.0718
3 0 five -0.526 10 -0.526 -0.526
4 1 three 0.0530 30 0.0530 0.0530
5 1 four -0.162 24 -0.162 -0.162
6 1 five -0.460 10 -0.460 -0.460
7 2 three 0.184 30 0.184 0.184
8 2 four 0.0960 24 0.0960 0.0960
9 2 five -0.00410 10 -0.00410 -0.00410
$Asia$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 0.00549 14 0.00549 0.00549
2 0 2 -0.00191 19 -0.00191 -0.00191
3 0 3 -0.00191 9 -0.00191 -0.00191
4 0 4 -0.00191 16 -0.00191 -0.00191
5 0 8 -0.00191 6 -0.00191 -0.00191
6 1 1 0.00549 18 0.00549 0.00549
7 1 2 -0.00377 16 -0.00377 -0.00377
8 1 3 -0.00377 13 -0.00377 -0.00377
9 1 4 -0.00377 15 -0.00377 -0.00377
10 1 8 -0.00377 2 -0.00377 -0.00377
11 2 1 0.00549 15 0.00549 0.00549
12 2 2 -0.0000232 13 -0.0000232 -0.0000232
13 2 3 -0.0000232 13 -0.0000232 -0.0000232
14 2 4 -0.0000232 20 -0.0000232 -0.0000232
15 2 8 -0.0000232 3 -0.0000232 -0.0000232
$Asia$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 0.0000319 1 0.0000319 0.0000319
2 0 1.94 0.00000854 7 0.00000854 0.00000854
3 0 2.46 -0.00000364 7 -0.00000364 -0.00000364
4 0 2.79 -0.00000366 7 -0.00000366 -0.00000366
5 0 3.19 -0.00000366 7 -0.00000366 -0.00000366
6 0 3.44 -0.00000366 7 -0.00000366 -0.00000366
7 0 3.52 -0.00000366 7 -0.00000366 -0.00000366
8 0 3.73 -0.00000366 7 -0.00000366 -0.00000366
9 0 4.05 -0.00000366 7 -0.00000366 -0.00000366
10 0 5.45 -0.00000366 7 -0.00000366 -0.00000366
# i 20 more rows
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 FALSE 1.20e-14 7 1.20e-14 1.20e-14
4 0 16.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
5 0 18.7 FALSE 1.20e-14 3 1.20e-14 1.20e-14
6 0 19.8 FALSE 1.20e-14 5 1.20e-14 1.20e-14
7 0 21.4 FALSE 1.20e-14 4 1.20e-14 1.20e-14
8 0 22.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 FALSE 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 FALSE 1.20e-14 5 1.20e-14 1.20e-14
4 0 16.4 FALSE 1.20e-14 5 1.20e-14 1.20e-14
5 0 18.7 FALSE 1.20e-14 7 1.20e-14 1.20e-14
6 0 19.8 FALSE 1.20e-14 5 1.20e-14 1.20e-14
7 0 21.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 FALSE 1.20e-14 5 1.20e-14 1.20e-14
9 0 30.1 FALSE 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 Lincoln Continental 1.20e-14 2 1.20e-14 1.20e-14
3 0 15.2 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
6 0 19.8 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
8 0 22.9 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
10 0 33.9 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
# i 950 more rows
$Asia$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 three 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 three 1.20e-14 5 1.20e-14 1.20e-14
4 0 16.4 three 1.20e-14 5 1.20e-14 1.20e-14
5 0 18.7 three 1.20e-14 5 1.20e-14 1.20e-14
6 0 19.8 three 1.20e-14 3 1.20e-14 1.20e-14
7 0 21.4 three 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 three 1.20e-14 3 1.20e-14 1.20e-14
9 0 30.1 three 1.20e-14 0 1.20e-14 1.20e-14
10 0 33.9 three 1.20e-14 0 1.20e-14 1.20e-14
# i 80 more rows
$Asia$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 1 1.20e-14 0 1.20e-14 1.20e-14
3 0 15.2 1 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 1 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 1 1.20e-14 2 1.20e-14 1.20e-14
6 0 19.8 1 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 1 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 1 1.20e-14 5 1.20e-14 1.20e-14
9 0 30.1 1 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 1 1.20e-14 4 1.20e-14 1.20e-14
# i 140 more rows
$Asia$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
3 0 15.2 1.50 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 1.50 1.20e-14 0 1.20e-14 1.20e-14
6 0 19.8 1.50 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
8 0 22.9 1.50 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 1.50 1.20e-14 1 1.20e-14 1.20e-14
10 0 33.9 1.50 1.20e-14 0 1.20e-14 1.20e-14
# i 290 more rows
$Asia$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
2 0 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
3 0 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
4 0 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
5 1 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
6 1 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
7 1 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
8 1 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
9 2 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
10 2 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
11 2 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
12 2 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 1.20e-14 2 1.20e-14 1.20e-14
2 0 TRUE Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
3 0 FALSE Cadillac Fleetwood 1.20e-14 2 1.20e-14 1.20e-14
4 0 TRUE Cadillac Fleetwood 1.20e-14 0 1.20e-14 1.20e-14
5 0 FALSE Merc 450SL 1.20e-14 2 1.20e-14 1.20e-14
6 0 TRUE Merc 450SL 1.20e-14 0 1.20e-14 1.20e-14
7 0 FALSE Merc 450SLC 1.20e-14 2 1.20e-14 1.20e-14
8 0 TRUE Merc 450SLC 1.20e-14 0 1.20e-14 1.20e-14
9 0 FALSE Camaro Z28 1.20e-14 2 1.20e-14 1.20e-14
10 0 TRUE Camaro Z28 1.20e-14 0 1.20e-14 1.20e-14
# i 182 more rows
$Asia$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
2 0 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
3 0 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
4 0 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
5 0 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
6 0 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
7 1 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
8 1 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
9 1 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
10 1 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
11 1 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
12 1 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
13 2 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
14 2 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
15 2 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
16 2 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
17 2 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
18 2 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 1.20e-14 0 1.20e-14 1.20e-14
2 0 TRUE 1 1.20e-14 14 1.20e-14 1.20e-14
3 0 FALSE 2 1.20e-14 10 1.20e-14 1.20e-14
4 0 TRUE 2 1.20e-14 9 1.20e-14 1.20e-14
5 0 FALSE 3 1.20e-14 8 1.20e-14 1.20e-14
6 0 TRUE 3 1.20e-14 1 1.20e-14 1.20e-14
7 0 FALSE 4 1.20e-14 13 1.20e-14 1.20e-14
8 0 TRUE 4 1.20e-14 3 1.20e-14 1.20e-14
9 0 FALSE 8 1.20e-14 5 1.20e-14 1.20e-14
10 0 TRUE 8 1.20e-14 1 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 1.20e-14 0 1.20e-14 1.20e-14
2 0 TRUE 1.50 1.20e-14 1 1.20e-14 1.20e-14
3 0 FALSE 1.94 1.20e-14 0 1.20e-14 1.20e-14
4 0 TRUE 1.94 1.20e-14 7 1.20e-14 1.20e-14
5 0 FALSE 2.46 1.20e-14 2 1.20e-14 1.20e-14
6 0 TRUE 2.46 1.20e-14 5 1.20e-14 1.20e-14
7 0 FALSE 2.79 1.20e-14 4 1.20e-14 1.20e-14
8 0 TRUE 2.79 1.20e-14 3 1.20e-14 1.20e-14
9 0 FALSE 3.19 1.20e-14 4 1.20e-14 1.20e-14
10 0 TRUE 3.19 1.20e-14 3 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0.106 2 0.106 0.106
2 0 TRUE Lincoln Continental 0.106 0 -1.38 -1.38
3 0 FALSE Cadillac Fleetwood 0.106 2 0.106 0.106
4 0 TRUE Cadillac Fleetwood 0.106 0 -1.38 -1.38
5 0 FALSE Merc 450SL 0.106 2 0.106 0.106
6 0 TRUE Merc 450SL 0.106 0 -1.38 -1.38
7 0 FALSE Merc 450SLC 0.106 2 0.106 0.106
8 0 TRUE Merc 450SLC 0.106 0 -1.38 -1.38
9 0 FALSE Camaro Z28 0.106 2 0.106 0.106
10 0 TRUE Camaro Z28 0.106 0 -1.38 -1.38
# i 182 more rows
$Asia$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.0688 30 -0.0688 -0.0688
2 0 TRUE three -0.0688 0 0.453 0.453
3 0 FALSE four -0.0688 8 -0.0688 -0.0688
4 0 TRUE four -0.466 16 0.0563 0.0563
5 0 FALSE five -0.0688 0 -0.0688 -0.0688
6 0 TRUE five -1.06 10 -0.541 -0.541
7 1 FALSE three -0.204 30 -0.204 -0.204
8 1 TRUE three -0.204 0 -0.104 -0.104
9 1 FALSE four -0.340 8 -0.340 -0.340
10 1 TRUE four -0.607 16 -0.507 -0.507
11 1 FALSE five -0.340 0 -0.340 -0.340
12 1 TRUE five -0.905 10 -0.806 -0.806
13 2 FALSE three -0.00554 30 -0.00554 -0.00554
14 2 TRUE three -0.00554 0 -0.628 -0.628
15 2 FALSE four -0.165 8 -0.165 -0.165
16 2 TRUE four -0.228 16 -0.850 -0.850
17 2 FALSE five -0.165 0 -0.165 -0.165
18 2 TRUE five -0.328 10 -0.950 -0.950
$Asia$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0.0166 6 0.0166 0.0166
2 0 TRUE 1 0.0166 8 -0.107 -0.107
3 0 FALSE 2 0.0166 12 -0.0662 -0.0662
4 0 TRUE 2 -0.00350 7 -0.213 -0.213
5 0 FALSE 3 0.0166 7 0.0203 0.0203
6 0 TRUE 3 -0.00342 2 -0.127 -0.127
7 0 FALSE 4 0.0166 11 -0.0662 -0.0662
8 0 TRUE 4 0.550 5 0.423 0.423
9 0 FALSE 8 0.0166 2 0.0203 0.0203
10 0 TRUE 8 0.550 4 0.340 0.340
# i 20 more rows
$Asia$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0.0139 0 0.0139 0.0139
2 0 TRUE 1.50 0.0139 1 -0.00322 -0.00322
3 0 FALSE 1.94 0.0139 0 0.00361 0.00361
4 0 TRUE 1.94 0.0138 7 -0.0244 -0.0244
5 0 FALSE 2.46 0.0139 1 -0.00732 -0.00732
6 0 TRUE 2.46 0.0138 6 -0.0245 -0.0245
7 0 FALSE 2.79 0.0139 1 -0.00732 -0.00732
8 0 TRUE 2.79 0.0378 6 0.0207 0.0207
9 0 FALSE 3.19 0.0139 3 0.0139 0.0139
10 0 TRUE 3.19 0.0761 4 0.0487 0.0487
# i 50 more rows
$Asia$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.184 2 -0.184 -0.184
2 0 Cadillac Fleetwood three -0.184 2 -0.184 -0.184
3 0 Merc 450SL three -0.184 2 -0.184 -0.184
4 0 Merc 450SLC three -0.184 2 -0.184 -0.184
5 0 Camaro Z28 three -0.184 2 -0.184 -0.184
6 0 Chrysler Imperial three -0.184 2 0.274 0.274
7 0 Duster 360 three -0.184 2 0.272 0.272
8 0 Merc 450SE three -0.184 2 0.250 0.250
9 0 Hornet Sportabout three -0.184 2 0.250 0.250
10 0 AMC Javelin three -0.184 2 -0.0198 -0.0198
# i 278 more rows
$Asia$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 3.42 0 3.42 3.42
2 0 Cadillac Fleetwood 1 3.42 0 3.42 3.42
3 0 Merc 450SL 1 3.42 0 3.42 3.42
4 0 Merc 450SLC 1 3.42 0 3.42 3.42
5 0 Camaro Z28 1 3.42 0 3.42 3.42
6 0 Chrysler Imperial 1 3.42 0 3.00 3.00
7 0 Duster 360 1 3.42 0 -0.418 -0.418
8 0 Merc 450SE 1 3.42 0 -3.32 -3.32
9 0 Hornet Sportabout 1 3.42 0 -3.74 -3.74
10 0 AMC Javelin 1 3.42 0 -4.12 -4.12
# i 470 more rows
$Asia$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.342 0 0.342 0.342
2 0 Cadillac Fleetwood 1.50 0.342 0 0.342 0.342
3 0 Merc 450SL 1.50 0.342 0 0.342 0.342
4 0 Merc 450SLC 1.50 0.342 0 0.342 0.342
5 0 Camaro Z28 1.50 0.342 0 0.342 0.342
6 0 Chrysler Imperial 1.50 0.342 0 0.342 0.342
7 0 Duster 360 1.50 0.342 0 0.284 0.284
8 0 Merc 450SE 1.50 0.342 0 -0.0324 -0.0324
9 0 Hornet Sportabout 1.50 0.342 0 -0.0439 -0.0439
10 0 AMC Javelin 1.50 0.342 0 -0.0520 -0.0520
# i 950 more rows
$Asia$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.0284 6 0.0284 0.0284
2 0 four 1 0.0284 8 -0.0211 -0.0211
3 0 five 1 0.0284 0 -0.0245 -0.0245
4 0 three 2 0.0284 8 -0.0766 -0.0766
5 0 four 2 0.162 7 0.0573 0.0573
6 0 five 2 0.130 4 0.130 0.130
7 0 three 3 0.0284 7 -0.0211 -0.0211
8 0 four 3 0.193 2 0.140 0.140
9 0 five 3 0.147 0 0.0422 0.0422
10 0 three 4 0.0284 8 -0.0767 -0.0767
# i 35 more rows
$Asia$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 0.0363 0 0.0363 0.0363
2 0 four 1.50 0.0363 0 0.0269 0.0269
3 0 five 1.50 0.0363 1 0.0202 0.0202
4 0 three 1.94 0.0363 0 0.00815 0.00815
5 0 four 1.94 0.0527 6 0.00226 0.00226
6 0 five 1.94 0.0535 1 0.00298 0.00298
7 0 three 2.46 0.0363 1 -0.0141 -0.0141
8 0 four 2.46 0.0599 4 0.00943 0.00943
9 0 five 2.46 0.0786 2 0.0281 0.0281
10 0 three 2.79 0.0363 1 -0.0141 -0.0141
# i 80 more rows
$Asia$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.00454 0 0.00454 0.00454
2 0 2 1.50 0.00454 1 -0.0287 -0.0287
3 0 3 1.50 0.00454 0 -0.0230 -0.0230
4 0 4 1.50 0.00454 0 -0.0329 -0.0329
5 0 8 1.50 0.00454 0 -0.0335 -0.0335
6 0 1 1.94 0.00454 4 -0.00377 -0.00377
7 0 2 1.94 0.0244 3 -0.0171 -0.0171
8 0 3 1.94 0.0244 0 -0.00721 -0.00721
9 0 4 1.94 0.0244 0 -0.0171 -0.0171
10 0 8 1.94 0.0244 0 -0.0171 -0.0171
# i 140 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 1.31e-14 7 1.31e-14 1.31e-14
4 0 16.4 1.31e-14 7 1.31e-14 1.31e-14
5 0 18.7 1.31e-14 7 1.31e-14 1.31e-14
6 0 19.8 1.31e-14 7 1.31e-14 1.31e-14
7 0 21.4 1.31e-14 7 1.31e-14 1.31e-14
8 0 22.9 1.31e-14 7 1.31e-14 1.31e-14
9 0 30.1 1.31e-14 7 1.31e-14 1.31e-14
10 0 33.9 1.31e-14 7 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.31e-14 36 1.31e-14 1.31e-14
2 0 TRUE 1.31e-14 28 1.31e-14 1.31e-14
3 1 FALSE 1.31e-14 36 1.31e-14 1.31e-14
4 1 TRUE 1.31e-14 28 1.31e-14 1.31e-14
5 2 FALSE 1.31e-14 36 1.31e-14 1.31e-14
6 2 TRUE 1.31e-14 28 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0.0432 38 0.0432 0.0432
2 0 TRUE -0.0632 26 -0.0632 -0.0632
3 1 FALSE -0.0321 38 -0.0321 -0.0321
4 1 TRUE 0.0101 26 0.0101 0.0101
5 2 FALSE -0.0297 38 -0.0297 -0.0297
6 2 TRUE 0.00670 26 0.00670 0.00670
$Europe$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.166 2 0.166 0.166
2 0 Cadillac Fleetwood 0.166 2 0.166 0.166
3 0 Merc 450SL 1.17 2 1.17 1.17
4 0 Merc 450SLC 1.17 2 1.17 1.17
5 0 Camaro Z28 0.166 2 0.166 0.166
6 0 Chrysler Imperial 0.166 2 0.166 0.166
7 0 Duster 360 0.166 2 0.166 0.166
8 0 Merc 450SE 1.17 2 1.17 1.17
9 0 Hornet Sportabout 0.166 2 0.166 0.166
10 0 AMC Javelin 0.166 2 0.166 0.166
# i 86 more rows
$Europe$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three -0.372 30 -0.372 -0.372
2 0 four 0.211 24 0.211 0.211
3 0 five 0.610 10 0.610 0.610
4 1 three -0.106 30 -0.106 -0.106
5 1 four 0.394 24 0.394 0.394
6 1 five 0.594 10 0.594 0.594
7 2 three -0.239 30 -0.239 -0.239
8 2 four 0.136 24 0.136 0.136
9 2 five -0.0638 10 -0.0638 -0.0638
$Europe$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 -0.00549 14 -0.00549 -0.00549
2 0 2 0.00191 19 0.00191 0.00191
3 0 3 0.00191 9 0.00191 0.00191
4 0 4 0.00191 16 0.00191 0.00191
5 0 8 0.00191 6 0.00191 0.00191
6 1 1 -0.00549 18 -0.00549 -0.00549
7 1 2 0.00377 16 0.00377 0.00377
8 1 3 0.00377 13 0.00377 0.00377
9 1 4 0.00377 15 0.00377 0.00377
10 1 8 0.00377 2 0.00377 0.00377
11 2 1 -0.00549 15 -0.00549 -0.00549
12 2 2 0.0000232 13 0.0000232 0.0000232
13 2 3 0.0000232 13 0.0000232 0.0000232
14 2 4 0.0000232 20 0.0000232 0.0000232
15 2 8 0.0000232 3 0.0000232 0.0000232
$Europe$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 -0.0000319 1 -0.0000319 -0.0000319
2 0 1.94 -0.00000854 7 -0.00000854 -0.00000854
3 0 2.46 0.00000364 7 0.00000364 0.00000364
4 0 2.79 0.00000366 7 0.00000366 0.00000366
5 0 3.19 0.00000366 7 0.00000366 0.00000366
6 0 3.44 0.00000366 7 0.00000366 0.00000366
7 0 3.52 0.00000366 7 0.00000366 0.00000366
8 0 3.73 0.00000366 7 0.00000366 0.00000366
9 0 4.05 0.00000366 7 0.00000366 0.00000366
10 0 5.45 0.00000366 7 0.00000366 0.00000366
# i 20 more rows
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 FALSE 1.31e-14 7 1.31e-14 1.31e-14
4 0 16.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
5 0 18.7 FALSE 1.31e-14 3 1.31e-14 1.31e-14
6 0 19.8 FALSE 1.31e-14 5 1.31e-14 1.31e-14
7 0 21.4 FALSE 1.31e-14 4 1.31e-14 1.31e-14
8 0 22.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 FALSE 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 FALSE 1.31e-14 5 1.31e-14 1.31e-14
4 0 16.4 FALSE 1.31e-14 5 1.31e-14 1.31e-14
5 0 18.7 FALSE 1.31e-14 7 1.31e-14 1.31e-14
6 0 19.8 FALSE 1.31e-14 5 1.31e-14 1.31e-14
7 0 21.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 FALSE 1.31e-14 5 1.31e-14 1.31e-14
9 0 30.1 FALSE 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 Lincoln Continental 1.31e-14 2 1.31e-14 1.31e-14
3 0 15.2 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
6 0 19.8 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
8 0 22.9 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
10 0 33.9 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
# i 950 more rows
$Europe$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 three 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 three 1.31e-14 5 1.31e-14 1.31e-14
4 0 16.4 three 1.31e-14 5 1.31e-14 1.31e-14
5 0 18.7 three 1.31e-14 5 1.31e-14 1.31e-14
6 0 19.8 three 1.31e-14 3 1.31e-14 1.31e-14
7 0 21.4 three 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 three 1.31e-14 3 1.31e-14 1.31e-14
9 0 30.1 three 1.31e-14 0 1.31e-14 1.31e-14
10 0 33.9 three 1.31e-14 0 1.31e-14 1.31e-14
# i 80 more rows
$Europe$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 1 1.31e-14 0 1.31e-14 1.31e-14
3 0 15.2 1 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 1 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 1 1.31e-14 2 1.31e-14 1.31e-14
6 0 19.8 1 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 1 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 1 1.31e-14 5 1.31e-14 1.31e-14
9 0 30.1 1 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 1 1.31e-14 4 1.31e-14 1.31e-14
# i 140 more rows
$Europe$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
3 0 15.2 1.50 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 1.50 1.31e-14 0 1.31e-14 1.31e-14
6 0 19.8 1.50 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
8 0 22.9 1.50 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 1.50 1.31e-14 1 1.31e-14 1.31e-14
10 0 33.9 1.50 1.31e-14 0 1.31e-14 1.31e-14
# i 290 more rows
$Europe$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
2 0 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
3 0 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
4 0 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
5 1 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
6 1 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
7 1 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
8 1 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
9 2 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
10 2 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
11 2 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
12 2 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 1.31e-14 2 1.31e-14 1.31e-14
2 0 TRUE Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
3 0 FALSE Cadillac Fleetwood 1.31e-14 2 1.31e-14 1.31e-14
4 0 TRUE Cadillac Fleetwood 1.31e-14 0 1.31e-14 1.31e-14
5 0 FALSE Merc 450SL 1.31e-14 2 1.31e-14 1.31e-14
6 0 TRUE Merc 450SL 1.31e-14 0 1.31e-14 1.31e-14
7 0 FALSE Merc 450SLC 1.31e-14 2 1.31e-14 1.31e-14
8 0 TRUE Merc 450SLC 1.31e-14 0 1.31e-14 1.31e-14
9 0 FALSE Camaro Z28 1.31e-14 2 1.31e-14 1.31e-14
10 0 TRUE Camaro Z28 1.31e-14 0 1.31e-14 1.31e-14
# i 182 more rows
$Europe$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
2 0 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
3 0 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
4 0 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
5 0 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
6 0 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
7 1 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
8 1 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
9 1 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
10 1 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
11 1 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
12 1 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
13 2 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
14 2 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
15 2 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
16 2 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
17 2 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
18 2 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 1.31e-14 0 1.31e-14 1.31e-14
2 0 TRUE 1 1.31e-14 14 1.31e-14 1.31e-14
3 0 FALSE 2 1.31e-14 10 1.31e-14 1.31e-14
4 0 TRUE 2 1.31e-14 9 1.31e-14 1.31e-14
5 0 FALSE 3 1.31e-14 8 1.31e-14 1.31e-14
6 0 TRUE 3 1.31e-14 1 1.31e-14 1.31e-14
7 0 FALSE 4 1.31e-14 13 1.31e-14 1.31e-14
8 0 TRUE 4 1.31e-14 3 1.31e-14 1.31e-14
9 0 FALSE 8 1.31e-14 5 1.31e-14 1.31e-14
10 0 TRUE 8 1.31e-14 1 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 1.31e-14 0 1.31e-14 1.31e-14
2 0 TRUE 1.50 1.31e-14 1 1.31e-14 1.31e-14
3 0 FALSE 1.94 1.31e-14 0 1.31e-14 1.31e-14
4 0 TRUE 1.94 1.31e-14 7 1.31e-14 1.31e-14
5 0 FALSE 2.46 1.31e-14 2 1.31e-14 1.31e-14
6 0 TRUE 2.46 1.31e-14 5 1.31e-14 1.31e-14
7 0 FALSE 2.79 1.31e-14 4 1.31e-14 1.31e-14
8 0 TRUE 2.79 1.31e-14 3 1.31e-14 1.31e-14
9 0 FALSE 3.19 1.31e-14 4 1.31e-14 1.31e-14
10 0 TRUE 3.19 1.31e-14 3 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0844 2 -1.57 -1.57
2 0 TRUE Lincoln Continental -0.0844 0 -0.0844 -0.0844
3 0 FALSE Cadillac Fleetwood -0.0844 2 -1.57 -1.57
4 0 TRUE Cadillac Fleetwood -0.0844 0 -0.0844 -0.0844
5 0 FALSE Merc 450SL -0.0844 2 -1.57 -1.57
6 0 TRUE Merc 450SL -0.0844 0 -0.0844 -0.0844
7 0 FALSE Merc 450SLC -0.0844 2 -1.57 -1.57
8 0 TRUE Merc 450SLC -0.0844 0 -0.0844 -0.0844
9 0 FALSE Camaro Z28 -0.0844 2 -1.57 -1.57
10 0 TRUE Camaro Z28 -0.0844 0 -0.0844 -0.0844
# i 182 more rows
$Europe$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.00407 30 0.386 0.386
2 0 TRUE three -0.00407 0 0.427 0.427
3 0 FALSE four -0.00407 8 0.386 0.386
4 0 TRUE four -0.177 16 0.254 0.254
5 0 FALSE five -0.00407 0 0.386 0.386
6 0 TRUE five 0.226 10 0.657 0.657
7 1 FALSE three -0.0677 30 -0.0254 -0.0254
8 1 TRUE three -0.0677 0 -0.212 -0.212
9 1 FALSE four -0.187 8 -0.145 -0.145
10 1 TRUE four -0.241 16 -0.385 -0.385
11 1 FALSE five -0.187 0 -0.145 -0.145
12 1 TRUE five -0.0395 10 -0.183 -0.183
13 2 FALSE three -0.0677 30 -0.500 -0.500
14 2 TRUE three -0.0677 0 -0.355 -0.355
15 2 FALSE four -0.176 8 -0.608 -0.608
16 2 TRUE four -0.175 16 -0.463 -0.463
17 2 FALSE five -0.176 0 -0.608 -0.608
18 2 TRUE five -0.0753 10 -0.363 -0.363
$Europe$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0166 6 -0.144 -0.144
2 0 TRUE 1 -0.0166 8 -0.0129 -0.0129
3 0 FALSE 2 -0.0166 12 -0.226 -0.226
4 0 TRUE 2 0.00350 7 0.00350 0.00350
5 0 FALSE 3 -0.0166 7 -0.140 -0.140
6 0 TRUE 3 0.00342 2 -0.0793 -0.0793
7 0 FALSE 4 -0.0166 11 -0.226 -0.226
8 0 TRUE 4 -0.550 5 -0.546 -0.546
9 0 FALSE 8 -0.0166 2 -0.140 -0.140
10 0 TRUE 8 -0.550 4 -0.633 -0.633
# i 20 more rows
$Europe$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 -0.0139 0 -0.0310 -0.0310
2 0 TRUE 1.50 -0.0139 1 -0.0138 -0.0138
3 0 FALSE 1.94 -0.0139 0 -0.0412 -0.0412
4 0 TRUE 1.94 -0.0138 7 -0.0350 -0.0350
5 0 FALSE 2.46 -0.0139 1 -0.0522 -0.0522
6 0 TRUE 2.46 -0.0138 6 -0.0350 -0.0350
7 0 FALSE 2.79 -0.0139 1 -0.0522 -0.0522
8 0 TRUE 2.79 -0.0378 6 -0.0378 -0.0378
9 0 FALSE 3.19 -0.0139 3 -0.0309 -0.0309
10 0 TRUE 3.19 -0.0761 4 -0.0973 -0.0973
# i 50 more rows
$Europe$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three 0.301 2 0.301 0.301
2 0 Cadillac Fleetwood three 0.301 2 0.301 0.301
3 0 Merc 450SL three 0.301 2 0.301 0.301
4 0 Merc 450SLC three 0.301 2 0.301 0.301
5 0 Camaro Z28 three 0.301 2 0.301 0.301
6 0 Chrysler Imperial three 0.301 2 0.759 0.759
7 0 Duster 360 three 0.301 2 0.729 0.729
8 0 Merc 450SE three 0.301 2 0.235 0.235
9 0 Hornet Sportabout three 0.301 2 0.794 0.794
10 0 AMC Javelin three 0.301 2 0.964 0.964
# i 278 more rows
$Europe$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 -3.89 0 -3.89 -3.89
2 0 Cadillac Fleetwood 1 -3.89 0 -3.89 -3.89
3 0 Merc 450SL 1 -3.89 0 -3.89 -3.89
4 0 Merc 450SLC 1 -3.89 0 -3.89 -3.89
5 0 Camaro Z28 1 -3.89 0 -3.89 -3.89
6 0 Chrysler Imperial 1 -3.89 0 -5.45 -5.45
7 0 Duster 360 1 -3.89 0 -8.67 -8.67
8 0 Merc 450SE 1 -3.89 0 -11.1 -11.1
9 0 Hornet Sportabout 1 -3.89 0 -11.1 -11.1
10 0 AMC Javelin 1 -3.89 0 -11.4 -11.4
# i 470 more rows
$Europe$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 -0.403 0 -0.403 -0.403
2 0 Cadillac Fleetwood 1.50 -0.403 0 -0.403 -0.403
3 0 Merc 450SL 1.50 -0.403 0 -0.403 -0.403
4 0 Merc 450SLC 1.50 -0.403 0 -0.403 -0.403
5 0 Camaro Z28 1.50 -0.403 0 -0.403 -0.403
6 0 Chrysler Imperial 1.50 -0.403 0 -0.460 -0.460
7 0 Duster 360 1.50 -0.403 0 -0.555 -0.555
8 0 Merc 450SE 1.50 -0.403 0 -0.778 -0.778
9 0 Hornet Sportabout 1.50 -0.403 0 -0.789 -0.789
10 0 AMC Javelin 1.50 -0.403 0 -0.797 -0.797
# i 950 more rows
$Europe$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.00218 6 -0.105 -0.105
2 0 four 1 0.00218 8 -0.155 -0.155
3 0 five 1 0.00218 0 -0.158 -0.158
4 0 three 2 0.00218 8 -0.210 -0.210
5 0 four 2 0.00237 7 -0.210 -0.210
6 0 five 2 0.0348 4 -0.0727 -0.0727
7 0 three 3 0.00218 7 -0.155 -0.155
8 0 four 3 0.00237 2 -0.158 -0.158
9 0 five 3 0.0510 0 -0.161 -0.161
10 0 three 4 0.00218 8 -0.210 -0.210
# i 35 more rows
$Europe$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.00213 0 -0.0423 -0.0423
2 0 four 1.50 -0.00213 0 -0.0517 -0.0517
3 0 five 1.50 -0.00213 1 -0.0584 -0.0584
4 0 three 1.94 -0.00213 0 -0.0705 -0.0705
5 0 four 1.94 -0.00210 6 -0.0927 -0.0927
6 0 five 1.94 -0.00282 1 -0.0935 -0.0935
7 0 three 2.46 -0.00213 1 -0.0928 -0.0928
8 0 four 2.46 -0.00208 4 -0.0927 -0.0927
9 0 five 2.46 -0.0208 2 -0.111 -0.111
10 0 three 2.79 -0.00213 1 -0.0928 -0.0928
# i 80 more rows
$Europe$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 -0.00454 0 -0.0278 -0.0278
2 0 2 1.50 -0.00454 1 -0.0377 -0.0377
3 0 3 1.50 -0.00454 0 -0.0420 -0.0420
4 0 4 1.50 -0.00454 0 -0.00941 -0.00941
5 0 8 1.50 -0.00454 0 -0.0426 -0.0426
6 0 1 1.94 -0.00454 4 -0.0361 -0.0361
7 0 2 1.94 -0.0244 3 -0.0659 -0.0659
8 0 3 1.94 -0.0244 0 -0.0659 -0.0659
9 0 4 1.94 -0.0244 0 -0.0327 -0.0327
10 0 8 1.94 -0.0244 0 -0.0659 -0.0659
# i 140 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 2.64e-14 7 2.64e-14 2.64e-14
4 0 16.4 2.64e-14 7 2.64e-14 2.64e-14
5 0 18.7 2.64e-14 7 2.64e-14 2.64e-14
6 0 19.8 2.64e-14 7 2.64e-14 2.64e-14
7 0 21.4 2.64e-14 7 2.64e-14 2.64e-14
8 0 22.9 2.64e-14 7 2.64e-14 2.64e-14
9 0 30.1 2.64e-14 7 2.64e-14 2.64e-14
10 0 33.9 2.64e-14 7 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 2.64e-14 36 2.64e-14 2.64e-14
2 0 TRUE 2.64e-14 28 2.64e-14 2.64e-14
3 1 FALSE 2.64e-14 36 2.64e-14 2.64e-14
4 1 TRUE 2.64e-14 28 2.64e-14 2.64e-14
5 2 FALSE 2.64e-14 36 2.64e-14 2.64e-14
6 2 TRUE 2.64e-14 28 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.52e-10 38 1.52e-10 1.52e-10
2 0 TRUE -2.23e-10 26 -2.23e-10 -2.23e-10
3 1 FALSE -9.71e-10 38 -9.71e-10 -9.71e-10
4 1 TRUE 7.39e-10 26 7.39e-10 7.39e-10
5 2 FALSE -8.52e-10 38 -8.52e-10 -8.52e-10
6 2 TRUE -4.71e-11 26 -4.71e-11 -4.71e-11
$`North America`$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.139 2 0.139 0.139
2 0 Cadillac Fleetwood 0.139 2 0.139 0.139
3 0 Merc 450SL -0.861 2 -0.861 -0.861
4 0 Merc 450SLC -0.861 2 -0.861 -0.861
5 0 Camaro Z28 0.139 2 0.139 0.139
6 0 Chrysler Imperial 0.139 2 0.139 0.139
7 0 Duster 360 0.139 2 0.139 0.139
8 0 Merc 450SE -0.861 2 -0.861 -0.861
9 0 Hornet Sportabout 0.139 2 0.139 0.139
10 0 AMC Javelin 0.139 2 0.139 0.139
# i 86 more rows
$`North America`$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.254 30 0.254 0.254
2 0 four -0.283 24 -0.283 -0.283
3 0 five -0.0843 10 -0.0843 -0.0843
4 1 three 0.0526 30 0.0526 0.0526
5 1 four -0.232 24 -0.232 -0.232
6 1 five -0.133 10 -0.133 -0.133
7 2 three 0.0544 30 0.0544 0.0544
8 2 four -0.232 24 -0.232 -0.232
9 2 five 0.0679 10 0.0679 0.0679
$`North America`$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.08e-7 14 1.08e-7 1.08e-7
2 0 2 -3.71e-8 19 -3.71e-8 -3.71e-8
3 0 3 -3.71e-8 9 -3.71e-8 -3.71e-8
4 0 4 -3.71e-8 16 -3.71e-8 -3.71e-8
5 0 8 -4.13e-8 6 -4.13e-8 -4.13e-8
6 1 1 1.08e-7 18 1.08e-7 1.08e-7
7 1 2 -7.21e-8 16 -7.21e-8 -7.21e-8
8 1 3 -7.21e-8 13 -7.21e-8 -7.21e-8
9 1 4 -7.21e-8 15 -7.21e-8 -7.21e-8
10 1 8 -8.38e-8 2 -8.38e-8 -8.38e-8
11 2 1 1.08e-7 15 1.08e-7 1.08e-7
12 2 2 1.40e-9 13 1.40e-9 1.40e-9
13 2 3 1.40e-9 13 1.40e-9 1.40e-9
14 2 4 1.40e-9 20 1.40e-9 1.40e-9
15 2 8 1.17e-9 3 1.17e-9 1.17e-9
$`North America`$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 6.60e-10 1 6.60e-10 6.60e-10
2 0 1.94 -8.90e-12 7 -8.90e-12 -8.90e-12
3 0 2.46 -4.95e-11 7 -4.95e-11 -4.95e-11
4 0 2.79 -4.95e-11 7 -4.95e-11 -4.95e-11
5 0 3.19 -4.95e-11 7 -4.95e-11 -4.95e-11
6 0 3.44 -4.87e-11 7 -4.87e-11 -4.87e-11
7 0 3.52 -4.87e-11 7 -4.87e-11 -4.87e-11
8 0 3.73 -4.87e-11 7 -4.87e-11 -4.87e-11
9 0 4.05 -4.87e-11 7 -4.87e-11 -4.87e-11
10 0 5.45 -4.87e-11 7 -4.87e-11 -4.87e-11
# i 20 more rows
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 FALSE 2.64e-14 7 2.64e-14 2.64e-14
4 0 16.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
5 0 18.7 FALSE 2.64e-14 3 2.64e-14 2.64e-14
6 0 19.8 FALSE 2.64e-14 5 2.64e-14 2.64e-14
7 0 21.4 FALSE 2.64e-14 4 2.64e-14 2.64e-14
8 0 22.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 FALSE 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 FALSE 2.64e-14 5 2.64e-14 2.64e-14
4 0 16.4 FALSE 2.64e-14 5 2.64e-14 2.64e-14
5 0 18.7 FALSE 2.64e-14 7 2.64e-14 2.64e-14
6 0 19.8 FALSE 2.64e-14 5 2.64e-14 2.64e-14
7 0 21.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 FALSE 2.64e-14 5 2.64e-14 2.64e-14
9 0 30.1 FALSE 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 Lincoln Continental 2.64e-14 2 2.64e-14 2.64e-14
3 0 15.2 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
6 0 19.8 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
8 0 22.9 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
10 0 33.9 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
# i 950 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 three 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 three 2.64e-14 5 2.64e-14 2.64e-14
4 0 16.4 three 2.64e-14 5 2.64e-14 2.64e-14
5 0 18.7 three 2.64e-14 5 2.64e-14 2.64e-14
6 0 19.8 three 2.64e-14 3 2.64e-14 2.64e-14
7 0 21.4 three 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 three 2.64e-14 3 2.64e-14 2.64e-14
9 0 30.1 three 2.64e-14 0 2.64e-14 2.64e-14
10 0 33.9 three 2.64e-14 0 2.64e-14 2.64e-14
# i 80 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 1 2.64e-14 0 2.64e-14 2.64e-14
3 0 15.2 1 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 1 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 1 2.64e-14 2 2.64e-14 2.64e-14
6 0 19.8 1 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 1 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 1 2.64e-14 5 2.64e-14 2.64e-14
9 0 30.1 1 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 1 2.64e-14 4 2.64e-14 2.64e-14
# i 140 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
3 0 15.2 1.50 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 1.50 2.64e-14 0 2.64e-14 2.64e-14
6 0 19.8 1.50 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
8 0 22.9 1.50 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 1.50 2.64e-14 1 2.64e-14 2.64e-14
10 0 33.9 1.50 2.64e-14 0 2.64e-14 2.64e-14
# i 290 more rows
$`North America`$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
2 0 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
3 0 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
4 0 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
5 1 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
6 1 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
7 1 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
8 1 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
9 2 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
10 2 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
11 2 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
12 2 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 2.64e-14 2 2.64e-14 2.64e-14
2 0 TRUE Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
3 0 FALSE Cadillac Fleetwood 2.64e-14 2 2.64e-14 2.64e-14
4 0 TRUE Cadillac Fleetwood 2.64e-14 0 2.64e-14 2.64e-14
5 0 FALSE Merc 450SL 2.64e-14 2 2.64e-14 2.64e-14
6 0 TRUE Merc 450SL 2.64e-14 0 2.64e-14 2.64e-14
7 0 FALSE Merc 450SLC 2.64e-14 2 2.64e-14 2.64e-14
8 0 TRUE Merc 450SLC 2.64e-14 0 2.64e-14 2.64e-14
9 0 FALSE Camaro Z28 2.64e-14 2 2.64e-14 2.64e-14
10 0 TRUE Camaro Z28 2.64e-14 0 2.64e-14 2.64e-14
# i 182 more rows
$`North America`$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
2 0 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
3 0 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
4 0 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
5 0 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
6 0 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
7 1 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
8 1 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
9 1 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
10 1 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
11 1 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
12 1 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
13 2 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
14 2 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
15 2 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
16 2 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
17 2 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
18 2 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 2.64e-14 0 2.64e-14 2.64e-14
2 0 TRUE 1 2.64e-14 14 2.64e-14 2.64e-14
3 0 FALSE 2 2.64e-14 10 2.64e-14 2.64e-14
4 0 TRUE 2 2.64e-14 9 2.64e-14 2.64e-14
5 0 FALSE 3 2.64e-14 8 2.64e-14 2.64e-14
6 0 TRUE 3 2.64e-14 1 2.64e-14 2.64e-14
7 0 FALSE 4 2.64e-14 13 2.64e-14 2.64e-14
8 0 TRUE 4 2.64e-14 3 2.64e-14 2.64e-14
9 0 FALSE 8 2.64e-14 5 2.64e-14 2.64e-14
10 0 TRUE 8 2.64e-14 1 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 2.64e-14 0 2.64e-14 2.64e-14
2 0 TRUE 1.50 2.64e-14 1 2.64e-14 2.64e-14
3 0 FALSE 1.94 2.64e-14 0 2.64e-14 2.64e-14
4 0 TRUE 1.94 2.64e-14 7 2.64e-14 2.64e-14
5 0 FALSE 2.46 2.64e-14 2 2.64e-14 2.64e-14
6 0 TRUE 2.46 2.64e-14 5 2.64e-14 2.64e-14
7 0 FALSE 2.79 2.64e-14 4 2.64e-14 2.64e-14
8 0 TRUE 2.79 2.64e-14 3 2.64e-14 2.64e-14
9 0 FALSE 3.19 2.64e-14 4 2.64e-14 2.64e-14
10 0 TRUE 3.19 2.64e-14 3 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0218 2 -0.0218 -0.0218
2 0 TRUE Lincoln Continental -0.0218 0 -1.51 -1.51
3 0 FALSE Cadillac Fleetwood -0.0218 2 -0.0218 -0.0218
4 0 TRUE Cadillac Fleetwood -0.0218 0 -1.51 -1.51
5 0 FALSE Merc 450SL -0.0218 2 -0.0218 -0.0218
6 0 TRUE Merc 450SL -0.0218 0 -1.51 -1.51
7 0 FALSE Merc 450SLC -0.0218 2 -0.0218 -0.0218
8 0 TRUE Merc 450SLC -0.0218 0 -1.51 -1.51
9 0 FALSE Camaro Z28 -0.0218 2 -0.0218 -0.0218
10 0 TRUE Camaro Z28 -0.0218 0 -1.51 -1.51
# i 182 more rows
$`North America`$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0.0729 30 0.205 0.205
2 0 TRUE three 0.0729 0 0.894 0.894
3 0 FALSE four 0.0729 8 0.205 0.205
4 0 TRUE four 0.643 16 1.46 1.46
5 0 FALSE five 0.0729 0 0.205 0.205
6 0 TRUE five 0.838 10 1.66 1.66
7 1 FALSE three 0.271 30 0.329 0.329
8 1 TRUE three 0.271 0 0.170 0.170
9 1 FALSE four 0.527 8 0.585 0.585
10 1 TRUE four 0.848 16 0.746 0.746
11 1 FALSE five 0.527 0 0.585 0.585
12 1 TRUE five 0.945 10 0.843 0.843
13 2 FALSE three 0.0732 30 -0.117 -0.117
14 2 TRUE three 0.0732 0 -0.646 -0.646
15 2 FALSE four 0.340 8 0.150 0.150
16 2 TRUE four 0.403 16 -0.317 -0.317
17 2 FALSE five 0.340 0 0.150 0.150
18 2 TRUE five 0.403 10 -0.317 -0.317
$`North America`$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0000000460 6 0.00370 0.00370
2 0 TRUE 1 -0.0000000460 8 -0.123 -0.123
3 0 FALSE 2 -0.0000000460 12 -0.0828 -0.0828
4 0 TRUE 2 -0.000000439 7 -0.127 -0.127
5 0 FALSE 3 -0.0000000460 7 0.00369 0.00369
6 0 TRUE 3 -0.000000439 2 -0.210 -0.210
7 0 FALSE 4 -0.0000000460 11 -0.0000000460 -0.0000000460
8 0 TRUE 4 -0.000000446 5 -0.123 -0.123
9 0 FALSE 8 -0.0000000460 2 -0.0828 -0.0828
10 0 TRUE 8 -0.000000447 4 -0.210 -0.210
# i 20 more rows
$`North America`$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 9.93e-11 0 1.17e- 5 1.17e- 5
2 0 TRUE 1.50 9.93e-11 1 -1.71e- 2 -1.71e- 2
3 0 FALSE 1.94 9.93e-11 0 -2.12e- 2 -2.12e- 2
4 0 TRUE 1.94 -5.71e-10 7 -3.83e- 2 -3.83e- 2
5 0 FALSE 2.46 9.93e-11 1 -2.12e- 2 -2.12e- 2
6 0 TRUE 2.46 -6.18e-10 6 -3.83e- 2 -3.83e- 2
7 0 FALSE 2.79 9.93e-11 1 9.93e-11 9.93e-11
8 0 TRUE 2.79 7.83e-10 6 -1.71e- 2 -1.71e- 2
9 0 FALSE 3.19 9.93e-11 3 -1.03e- 2 -1.03e- 2
10 0 TRUE 3.19 7.86e-10 4 -3.83e- 2 -3.83e- 2
# i 50 more rows
$`North America`$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.117 2 -0.117 -0.117
2 0 Cadillac Fleetwood three -0.117 2 -0.117 -0.117
3 0 Merc 450SL three -0.117 2 -0.117 -0.117
4 0 Merc 450SLC three -0.117 2 -0.117 -0.117
5 0 Camaro Z28 three -0.117 2 -0.117 -0.117
6 0 Chrysler Imperial three -0.117 2 0.339 0.339
7 0 Duster 360 three -0.117 2 0.317 0.317
8 0 Merc 450SE three -0.117 2 0.317 0.317
9 0 Hornet Sportabout three -0.117 2 0.434 0.434
10 0 AMC Javelin three -0.117 2 0.547 0.547
# i 278 more rows
$`North America`$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 0.471 0 0.471 0.471
2 0 Cadillac Fleetwood 1 0.471 0 0.471 0.471
3 0 Merc 450SL 1 0.471 0 0.471 0.471
4 0 Merc 450SLC 1 0.471 0 0.471 0.471
5 0 Camaro Z28 1 0.471 0 0.471 0.471
6 0 Chrysler Imperial 1 0.471 0 -1.53 -1.53
7 0 Duster 360 1 0.471 0 -5.55 -5.55
8 0 Merc 450SE 1 0.471 0 -6.69 -6.69
9 0 Hornet Sportabout 1 0.471 0 -6.97 -6.97
10 0 AMC Javelin 1 0.471 0 -7.07 -7.07
# i 470 more rows
$`North America`$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.0603 0 0.0603 0.0603
2 0 Cadillac Fleetwood 1.50 0.0603 0 0.0603 0.0603
3 0 Merc 450SL 1.50 0.0603 0 0.0603 0.0603
4 0 Merc 450SLC 1.50 0.0603 0 0.0603 0.0603
5 0 Camaro Z28 1.50 0.0603 0 0.0602 0.0602
6 0 Chrysler Imperial 1.50 0.0603 0 0.00250 0.00250
7 0 Duster 360 1.50 0.0603 0 -0.252 -0.252
8 0 Merc 450SE 1.50 0.0603 0 -0.318 -0.318
9 0 Hornet Sportabout 1.50 0.0603 0 -0.334 -0.334
10 0 AMC Javelin 1.50 0.0603 0 -0.334 -0.334
# i 950 more rows
$`North America`$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 -0.0306 6 -0.108 -0.108
2 0 four 1 -0.0306 8 -0.157 -0.157
3 0 five 1 -0.0306 0 -0.160 -0.160
4 0 three 2 -0.0306 8 -0.213 -0.213
5 0 four 2 -0.165 7 -0.347 -0.347
6 0 five 2 -0.165 4 -0.242 -0.242
7 0 three 3 -0.0306 7 -0.157 -0.157
8 0 four 3 -0.195 2 -0.325 -0.325
9 0 five 3 -0.198 0 -0.380 -0.380
10 0 three 4 -0.0306 8 -0.213 -0.213
# i 35 more rows
$`North America`$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.0342 0 -0.0903 -0.0903
2 0 four 1.50 -0.0342 0 -0.0997 -0.0997
3 0 five 1.50 -0.0342 1 -0.106 -0.106
4 0 three 1.94 -0.0342 0 -0.119 -0.119
5 0 four 1.94 -0.0506 6 -0.157 -0.157
6 0 five 1.94 -0.0506 1 -0.157 -0.157
7 0 three 2.46 -0.0342 1 -0.141 -0.141
8 0 four 2.46 -0.0578 4 -0.164 -0.164
9 0 five 2.46 -0.0578 2 -0.164 -0.164
10 0 three 2.79 -0.0342 1 -0.141 -0.141
# i 80 more rows
$`North America`$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.0000000171 0 -0.0332 -0.0332
2 0 2 1.50 0.0000000171 1 -0.00425 -0.00425
3 0 3 1.50 0.0000000171 0 -0.0375 -0.0375
4 0 4 1.50 0.0000000171 0 -0.0281 -0.0281
5 0 8 1.50 0.0000000171 0 -0.0381 -0.0381
6 0 1 1.94 0.0000000171 4 -0.0415 -0.0415
7 0 2 1.94 0.000000145 3 -0.00831 -0.00831
8 0 3 1.94 0.000000145 0 -0.0415 -0.0415
9 0 4 1.94 0.000000145 0 -0.0316 -0.0316
10 0 8 1.94 0.000000145 0 -0.0415 -0.0415
# i 140 more rows
Code
get(cat_cars_ale, stats = "estimate")
Output
$Asia
$Asia$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 0.0218 -0.00838 0.0309 38.9 -50 31.2
4 model 1.17 -1.94 1.05 43.0 -50 40.6
5 gear 0.140 -0.232 0.119 37.7 -50 31.2
6 carb 0.00172 -0.00189 0.00549 40.9 -50 31.2
7 wt 0.0000118 -0.0000118 0.0000319 47.8 -50 31.2
$Asia$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
$Europe
$Europe$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 0.0218 -0.0309 0.00838 32.2 -50 6.25
4 model 2.27 -0.680 4.28 45.9 -21.9 50
5 gear 0.232 -0.172 0.365 30.2 -50 6.25
6 carb 0.00172 -0.00549 0.00189 27.4 -50 6.25
7 wt 0.0000118 -0.0000319 0.0000118 10.7 -50 6.25
$Europe$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
$`North America`
$`North America`$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 7.01e-10 -9.12e-10 3.46e-10 41.7 -50 -20.3
4 model 1.48e+ 0 -2.59e+ 0 -1.11e- 1 43.0 -50 -18.8
5 gear 1.28e- 1 -2.32e- 1 6.02e- 2 29.5 -50 12.5
6 carb 3.40e- 8 -4.13e- 8 1.08e- 7 20.4 -20.3 12.5
7 wt 3.83e-10 3.53e-10 6.60e-10 8.06 7.81 9.38
$`North America`$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
Code
get(cat_cars_ale, cats = c("Asia", "Europe"))
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
$Asia$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
$Asia$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
$Asia$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
$Europe$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
$Europe$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
$Europe$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
Code
get(cat_cars_ale, exclude_cols = list(d2_all = "am"))
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 28 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.71e-11 7.00e-10
$`North America`$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
$`North America`$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
$`North America`$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
$`North America`$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.47e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.35e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.28e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 Lincoln Contine~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 310 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 three 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 40 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 90 more rows
$`North America`$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE FALSE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE TRUE 12 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE TRUE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE Lincoln Continent~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE Cadillac Fleetwood 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE Cadillac Fleetwood 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE Merc 450SL 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE Merc 450SL 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE Merc 450SLC 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE Merc 450SLC 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE Camaro Z28 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE Camaro Z28 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 54 more rows
$`North America`$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE three 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE four 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE four 20 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE five 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE five 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 2 10 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 2 9 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 3 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 3 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 4 13 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 4 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 8 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 8 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 1.94 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 1.94 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 2.46 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 2.46 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 2.79 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 2.79 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 3.19 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 3.19 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 FALSE 3.44 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 TRUE 3.44 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 FALSE 3.52 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 TRUE 3.52 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 FALSE 3.73 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 TRUE 3.73 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 FALSE 4.05 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 TRUE 4.05 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 FALSE 5.45 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 TRUE 5.45 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
$`North America`$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
$`North America`$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
$`North America`$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
$`North America`$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
$`North America`$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
Code
get(cat_cars_ale, stats = "conf_regions")
Message
! Note that confidence regions are not reliable with fewer than 100 bootstrap iterations or p-values based on fewer than 100 random iterations.
i There are 2 bootstrap iterations.
i p-values are based on 100 iterations.
Output
$Asia
$Asia$d1
# A tibble: 45 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 1.20e-14
2 vs FALSE NA NA NA 36 56.2 1.20e-14 NA
3 vs TRUE NA NA NA 28 43.8 1.20e-14 NA
4 am FALSE NA NA NA 38 59.4 6.19e- 3 NA
5 am TRUE NA NA NA 26 40.6 1.55e- 2 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 -2.26e- 2 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 9.32e- 2 NA
8 model Merc 450SL NA NA NA 2 3.12 -2.35e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 -2.35e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 9.75e- 2 NA
# i 35 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$Asia$d2
# A tibble: 315 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 1.20e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 1.20e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 1.20e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 1.20e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 1.20e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 1.20e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 1.20e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 1.20e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 1.20e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 1.20e-14
# i 305 more rows
$Europe
$Europe$d1
# A tibble: 45 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 1.31e-14
2 vs FALSE NA NA NA 36 56.2 1.31e-14 NA
3 vs TRUE NA NA NA 28 43.8 1.31e-14 NA
4 am FALSE NA NA NA 38 59.4 -6.19e- 3 NA
5 am TRUE NA NA NA 26 40.6 -1.55e- 2 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 2.17e- 1 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 2.68e- 1 NA
8 model Merc 450SL NA NA NA 2 3.12 9.31e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 7.64e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 -6.88e- 2 NA
# i 35 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$Europe$d2
# A tibble: 310 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 1.31e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 1.31e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 1.31e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 1.31e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 1.31e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 1.31e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 1.31e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 1.31e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 1.31e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 1.31e-14
# i 300 more rows
$`North America`
$`North America`$d1
# A tibble: 44 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 2.64e-14
2 vs FALSE NA NA NA 36 56.2 2.64e-14 NA
3 vs TRUE NA NA NA 28 43.8 2.64e-14 NA
4 am FALSE NA NA NA 38 59.4 -5.57e-10 NA
5 am TRUE NA NA NA 26 40.6 1.57e-10 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 -1.95e- 1 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 -3.61e- 1 NA
8 model Merc 450SL NA NA NA 2 3.12 -6.95e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 -5.29e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 -2.87e- 2 NA
# i 34 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$`North America`$d2
# A tibble: 307 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 2.64e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 2.64e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 2.64e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 2.64e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 2.64e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 2.64e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 2.64e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 2.64e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 2.64e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 2.64e-14
# i 297 more rows
Code
get(cat_cars_ale, stats = "conf_sig")
Message
! Note that confidence regions are not reliable with fewer than 100 bootstrap iterations or p-values based on fewer than 100 random iterations.
i There are 2 bootstrap iterations.
i p-values are based on 100 iterations.
Output
$Asia
$Asia$d1
# A tibble: 8 x 12
term x start_x end_x x_span_pct n pct y start_y end_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 gear three NA NA NA 30 46.9 0.118 NA NA
2 gear four NA NA NA 24 37.5 0.00198 NA NA
3 gear five NA NA NA 10 15.6 -0.330 NA NA
4 carb <NA> 1 1 0 14 21.9 NA 0.00549 5.49e-3
5 carb <NA> 2 8 85.7 50 78.1 NA -0.00190 -1.90e-3
6 wt <NA> 1.50 1.50 0 1 1.56 NA 0.0000319 3.19e-5
7 wt <NA> 1.94 1.94 0 7 10.9 NA -0.00000143 -1.43e-6
8 wt <NA> 2.46 5.45 75.6 56 87.5 NA -0.00000906 -9.07e-6
# i 2 more variables: trend <dbl>, aler_band <ord>
$Asia$d2
# A tibble: 128 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE gear three below 30 46.9 -0.0927
2 am FALSE gear four below 8 12.5 -0.191
3 am TRUE gear four overlap 16 25 -0.433
4 am TRUE gear five below 10 15.6 -0.766
5 am FALSE carb [1,2] above 6 9.38 0.0166
6 am TRUE carb [1,2] overlap 15 23.4 0.00816
7 am FALSE carb [1,2] overlap 12 18.8 0.0134
8 am FALSE carb (2,4] above 7 10.9 0.0134
9 am TRUE carb (2,4] overlap 2 3.12 -0.000226
10 am FALSE carb (2,4] overlap 11 17.2 0.190
# i 118 more rows
$Europe
$Europe$d1
# A tibble: 40 x 12
term x start_x end_x x_span_pct n pct y start_y end_y trend
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 model Linco~ NA NA NA 2 3.12 0.217 NA NA NA
2 model Cadil~ NA NA NA 2 3.12 0.268 NA NA NA
3 model Merc ~ NA NA NA 2 3.12 0.931 NA NA NA
4 model Merc ~ NA NA NA 2 3.12 0.764 NA NA NA
5 model Camar~ NA NA NA 2 3.12 -0.0688 NA NA NA
6 model Chrys~ NA NA NA 2 3.12 0.0978 NA NA NA
7 model Duste~ NA NA NA 2 3.12 0.0978 NA NA NA
8 model Merc ~ NA NA NA 2 3.12 0.596 NA NA NA
9 model Horne~ NA NA NA 2 3.12 -0.237 NA NA NA
10 model AMC J~ NA NA NA 2 3.12 -0.0841 NA NA NA
# i 30 more rows
# i 1 more variable: aler_band <ord>
$Europe$d2
# A tibble: 133 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE carb [1,2] overlap 18 28.1 -0.0150
2 am TRUE carb [1,2] below 8 12.5 -0.0166
3 am TRUE carb [1,2] overlap 7 10.9 0.000253
4 am FALSE carb (2,4] overlap 7 10.9 -0.0134
5 am TRUE carb (2,4] overlap 2 3.12 0.000227
6 am FALSE carb (2,4] below 11 17.2 -0.190
7 am TRUE carb (2,4] below 5 7.81 -0.454
8 am FALSE carb (4,8] below 2 3.12 -0.190
9 am TRUE carb (4,8] below 4 6.25 -0.454
10 am TRUE wt [1.5,2.79] below 7 10.9 -0.0338
# i 123 more rows
$`North America`
$`North America`$d1
# A tibble: 37 x 12
term x start_x end_x x_span_pct n pct y start_y end_y trend
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 model Linco~ NA NA NA 2 3.12 -0.195 NA NA NA
2 model Cadil~ NA NA NA 2 3.12 -0.361 NA NA NA
3 model Merc ~ NA NA NA 2 3.12 -0.695 NA NA NA
4 model Merc ~ NA NA NA 2 3.12 -0.529 NA NA NA
5 model Camar~ NA NA NA 2 3.12 -0.0287 NA NA NA
6 model Chrys~ NA NA NA 2 3.12 -0.362 NA NA NA
7 model Duste~ NA NA NA 2 3.12 -0.362 NA NA NA
8 model Merc ~ NA NA NA 2 3.12 -0.862 NA NA NA
9 model Horne~ NA NA NA 2 3.12 -0.195 NA NA NA
10 model AMC J~ NA NA NA 2 3.12 -0.362 NA NA NA
# i 27 more rows
# i 1 more variable: aler_band <ord>
$`North America`$d2
# A tibble: 167 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE model Lincoln Continental below 2 3.12 -0.0218
2 am FALSE model Cadillac Fleetwood below 2 3.12 -0.0218
3 am FALSE model Merc 450SL below 2 3.12 -0.0226
4 am FALSE model Merc 450SLC below 2 3.12 -0.0226
5 am FALSE model Camaro Z28 below 2 3.12 -0.0226
6 am FALSE model Chrysler Imperial overlap 2 3.12 -0.0226
7 am FALSE model Duster 360 overlap 2 3.12 -0.0226
8 am FALSE model Merc 450SE overlap 2 3.12 -0.0230
9 am FALSE model Hornet Sportabout overlap 2 3.12 -0.0230
10 am FALSE model AMC Javelin overlap 2 3.12 -0.0230
# i 157 more rows
Code
ale_plots_to_data(cat_cars_ale_plots)
Condition
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
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Warning in `regularize.values()`:
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Warning in `regularize.values()`:
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Warning in `regularize.values()`:
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Warning in `regularize.values()`:
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Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
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Warning in `regularize.values()`:
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Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
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Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
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Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
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Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning:
Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Warning in `regularize.values()`:
collapsing to unique 'x' values
Output
$Asia
$Asia$d1
$Asia$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 1.19744e-14
2 -Inf Inf 1.197341e-14 1.19744e-14
3 -Inf Inf 1.197341e-14 1.19744e-14
4 -Inf Inf 1.197341e-14 1.19744e-14
5 -Inf Inf 1.197341e-14 1.19744e-14
6 -Inf Inf 1.197341e-14 1.19744e-14
7 -Inf Inf 1.197341e-14 1.19744e-14
8 -Inf Inf 1.197341e-14 1.19744e-14
9 -Inf Inf 1.197341e-14 1.19744e-14
10 -Inf Inf 1.197341e-14 1.19744e-14
$Asia$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.197411e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.197411e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
$Asia$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 0.006188871 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 0.015482442 1 2 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.197341e-14 1.19744e-14
2 1.197341e-14 1.19744e-14
$Asia$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.02256553 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.09317291 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.23524785 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.23524785 1 4 NA lightgray 0.5 1 NA -Inf
5 5 0.09751143 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.26417810 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.26417810 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.26611036 1 8 NA lightgray 0.5 1 NA -Inf
9 9 0.43273667 1 9 NA lightgray 0.5 1 NA -Inf
10 10 0.44616466 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.44616466 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.44616466 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.44616466 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.77949799 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.22030910 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.05364243 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -0.05370066 1 17 NA lightgray 0.5 1 NA -Inf
18 18 0.11220276 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.11220281 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.14577267 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -0.02754194 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -0.36770131 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -0.03661493 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -0.69864333 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -0.69864332 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -0.69864353 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.02590338 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -0.52590572 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.02589030 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -0.85922071 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -0.52591213 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.02590860 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
4 Inf 1.197341e-14 1.19744e-14
5 Inf 1.197341e-14 1.19744e-14
6 Inf 1.197341e-14 1.19744e-14
7 Inf 1.197341e-14 1.19744e-14
8 Inf 1.197341e-14 1.19744e-14
9 Inf 1.197341e-14 1.19744e-14
10 Inf 1.197341e-14 1.19744e-14
11 Inf 1.197341e-14 1.19744e-14
12 Inf 1.197341e-14 1.19744e-14
13 Inf 1.197341e-14 1.19744e-14
14 Inf 1.197341e-14 1.19744e-14
15 Inf 1.197341e-14 1.19744e-14
16 Inf 1.197341e-14 1.19744e-14
17 Inf 1.197341e-14 1.19744e-14
18 Inf 1.197341e-14 1.19744e-14
19 Inf 1.197341e-14 1.19744e-14
20 Inf 1.197341e-14 1.19744e-14
21 Inf 1.197341e-14 1.19744e-14
22 Inf 1.197341e-14 1.19744e-14
23 Inf 1.197341e-14 1.19744e-14
24 Inf 1.197341e-14 1.19744e-14
25 Inf 1.197341e-14 1.19744e-14
26 Inf 1.197341e-14 1.19744e-14
27 Inf 1.197341e-14 1.19744e-14
28 Inf 1.197341e-14 1.19744e-14
29 Inf 1.197341e-14 1.19744e-14
30 Inf 1.197341e-14 1.19744e-14
31 Inf 1.197341e-14 1.19744e-14
32 Inf 1.197341e-14 1.19744e-14
$Asia$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.330118650 1 3 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
$Asia$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.005489234 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -0.001898356 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
4 Inf 1.197341e-14 1.19744e-14
5 Inf 1.197341e-14 1.19744e-14
$Asia$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 3.193949e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 -1.434368e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 -9.061987e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 1.19744e-14
2 -Inf Inf 1.197341e-14 1.19744e-14
3 -Inf Inf 1.197341e-14 1.19744e-14
4 -Inf Inf 1.197341e-14 1.19744e-14
5 -Inf Inf 1.197341e-14 1.19744e-14
6 -Inf Inf 1.197341e-14 1.19744e-14
7 -Inf Inf 1.197341e-14 1.19744e-14
8 -Inf Inf 1.197341e-14 1.19744e-14
9 -Inf Inf 1.197341e-14 1.19744e-14
10 -Inf Inf 1.197341e-14 1.19744e-14
$Asia$d2
$Asia$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
302 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
303 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
304 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
307 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
313 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
316 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
322 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
331 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
332 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
333 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
334 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
335 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
337 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
340 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
342 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
343 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
344 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
345 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
346 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
347 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
348 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
349 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
350 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
351 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
352 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
353 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
358 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
359 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
360 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
361 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
362 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
363 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
364 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
365 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
366 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
367 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
368 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
369 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
370 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
371 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
372 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
373 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
374 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
375 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
376 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
377 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
378 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
379 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
380 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
381 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
382 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
383 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
384 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
385 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
386 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
387 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
388 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
389 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
390 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
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392 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
393 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
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395 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
396 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
397 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
398 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
399 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
400 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
401 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
402 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
403 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
404 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
405 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
406 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
407 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
408 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
409 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
410 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
411 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
412 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
413 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
414 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
415 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
416 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
417 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
418 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
419 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
420 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
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423 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
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425 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
426 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
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428 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
429 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
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431 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
432 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
433 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
434 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
435 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
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437 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
438 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
439 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
440 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
441 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
442 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
443 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
445 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
446 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
447 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
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459 #FFBBBB 3 16 1 80 2.5 3.5 15.5 16.5 NA 0.1 1
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464 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
465 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
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467 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
468 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
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470 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
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476 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
477 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
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486 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
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488 #FFBBBB 3 17 1 81 2.5 3.5 16.5 17.5 NA 0.1 1
489 #FFBBBB 3 17 1 81 2.5 3.5 16.5 17.5 NA 0.1 1
490 #FFBBBB 4 17 1 113 3.5 4.5 16.5 17.5 NA 0.1 1
491 #FFBBBB 4 17 1 113 3.5 4.5 16.5 17.5 NA 0.1 1
492 #FFBBBB 4 17 1 113 3.5 4.5 16.5 17.5 NA 0.1 1
493 #FFBBBB 5 17 1 145 4.5 5.5 16.5 17.5 NA 0.1 1
494 #FFBBBB 5 17 1 145 4.5 5.5 16.5 17.5 NA 0.1 1
495 #FFBBBB 5 17 1 145 4.5 5.5 16.5 17.5 NA 0.1 1
496 #FFBBBB 6 17 1 177 5.5 6.5 16.5 17.5 NA 0.1 1
497 #FFBBBB 6 17 1 177 5.5 6.5 16.5 17.5 NA 0.1 1
498 #FFBBBB 6 17 1 177 5.5 6.5 16.5 17.5 NA 0.1 1
499 #FFBBBB 7 17 1 209 6.5 7.5 16.5 17.5 NA 0.1 1
500 #FFBBBB 7 17 1 209 6.5 7.5 16.5 17.5 NA 0.1 1
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$Asia$d2$`mpg:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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width height
1 NA NA
2 NA NA
3 NA NA
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5 NA NA
6 NA NA
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81 NA NA
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83 NA NA
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86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Asia$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
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24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
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88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Asia$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
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195 NA NA NA
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213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
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219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
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$Asia$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
$Asia$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Asia$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Asia$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Asia$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBBEFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBDBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC5FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #1508FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #A696FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBDBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBC1FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #0000FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBC9FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #A696FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBDBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBC9FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #9984FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFC6BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #A696FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBDBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBC9FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
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alpha width height
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$Asia$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF7F6A 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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8 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
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14 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
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width height
1 NA NA
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$Asia$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF9C8F 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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25 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
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28 #BAB9FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
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width height
1 NA NA
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$Asia$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF0100 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFC1BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #BBC9FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #BBC7FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFC4BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #BBC7FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFC4BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #BBC6FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #BBC6FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #BBC5FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFC4BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #BBC6FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFC4BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #BBC6FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBBDFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC5BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCABB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBBDFF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFCBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBBDFF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFCBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC0FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #957EFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC5FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #9781FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC6BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC5FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #6242FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC8BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC5FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #866AFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC8BB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC5FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #866AFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC2BB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFCCBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #6748FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC2BB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFCCBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #6748FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC6BB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFCCBB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #B6B1FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFCABB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #B6B1FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFCABB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #B6B1FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFCABB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #B6B1FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFCABB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #B6B1FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFBDBB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFCABB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #8E74FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFBFBB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFCABB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #A89AFF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC2BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFCABB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #AA9DFF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFBDBB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFCABB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #9780FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFBCBB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFCABB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #8F75FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFBFBB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFCABB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #9279FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFBFBB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFCABB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #9D88FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFBFBB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFCABB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #9D88FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFBFBB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFCABB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #9D88FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFBFBB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFCABB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #9D88FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFBFBB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFCABB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #9D88FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFB5B2 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFCABB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #6445FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFBBBB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFCABB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #6A4AFF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFBBBB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFCABB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #6A4AFF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFB0AB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFCABB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #5335FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFB0AB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFCABB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #3921FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCBFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBBDFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC5BB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFCABB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBBDFF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFCBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBBDFF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFCBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC0FF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #957EFF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC5FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #9781FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC6BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC5FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #6242FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC8BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC5FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #866AFF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC8BB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC5FF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #866AFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC2BB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFCCBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #6748FF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC2BB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFCCBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #6748FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC6BB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFCCBB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #B6B1FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC3BB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFCABB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #B6B1FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC3BB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFCABB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #B6B1FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC2BB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFCABB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #B5B0FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC2BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFCABB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #B5B0FF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFBDBB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFCABB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
147 #8D73FF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFBFBB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFCABB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
150 #A798FF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC2BB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFCABB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
153 #A99CFF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFBDBB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC9BB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
156 #BBBDFF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFBCBB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC8BB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
159 #BAB9FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFBBBB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC6BB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBCAFF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFB7B5 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC8BB 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
165 #5B3CFF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFAAA3 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC4BB 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBBFFF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFAAA3 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC4BB 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
171 #BBBFFF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFA59B 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC3BB 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
174 #BBC0FF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FF988B 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC1BB 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
177 #BBC4FF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FF8E7D 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC4BB 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
180 #5134FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FF9485 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC1BB 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
183 #BBBEFF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF9586 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC2BB 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
186 #BBBDFF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF816C 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC3BB 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
189 #3A21FF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FF654C 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC1BB 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
192 #0000FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCBFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBBDFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC5BB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFCABB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBBDFF 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC8BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFCBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBBDFF 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFCBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBC0FF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
207 #957EFF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFC8BB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBC5FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
210 #9781FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFC6BB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBC5FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
213 #6242FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFC8BB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBC5FF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
216 #866AFF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFC8BB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBC5FF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
219 #866AFF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFC2BB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFCCBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
222 #6748FF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC2BB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFCCBB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
225 #6748FF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC6BB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFCCBB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
228 #B6B1FF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC3BB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
230 #FFCABB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
231 #B6B1FF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
232 #FFC3BB 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
233 #FFCABB 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
234 #B6B1FF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFC2BB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
236 #FFCABB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
237 #B5B0FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
238 #FFC2BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
239 #FFC8BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
240 #B4ADFF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
241 #FFBDBB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC8BB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
243 #8B70FF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
244 #FFBFBB 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
245 #FFC8BB 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
246 #A696FF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
247 #FFBEBB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
248 #FFC7BB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
249 #A899FF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
250 #FFADA7 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
251 #FFC5BB 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
252 #BBBCFF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
253 #FF9E92 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
254 #FFC4BB 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
255 #B9B6FF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
256 #FF9687 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
257 #FFC1BB 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
258 #BBC9FF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
259 #FF8C7A 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
260 #FFC2BB 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
261 #BBBBFF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
262 #FF7B65 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
263 #FFBDBB 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
264 #FFC9BB 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
265 #FF7963 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
266 #FFBDBB 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
267 #FFC9BB 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
268 #FF725B 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
269 #FFBDBB 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
270 #FFC9BB 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
271 #FF644B 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
272 #FFBBBB 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
273 #FFC8BB 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
274 #FF573D 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
275 #FFBEBB 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
276 #B7B4FF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
277 #FF5F45 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
278 #FFB9B8 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
279 #FFC9BB 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
280 #FF6046 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
281 #FFBCBB 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
282 #FFCABB 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
283 #FF452C 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
284 #FFBDBB 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
285 #B0A7FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
286 #FF0000 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
287 #FFBABA 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
288 #A799FF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$Asia$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC5FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC4FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC2FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFCABB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC4FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #B5B1FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFB1AC 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC4FF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #A493FF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFA79F 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC4FF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #A18FFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FF9E92 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC4FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #9E8BFF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FF9E92 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC4FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #9E8BFF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBCAFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC4FF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBBEFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBCAFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBC4FF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBBEFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBCAFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC4FF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBBEFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBCAFF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC4FF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBBEFF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBCAFF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBC4FF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBBEFF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC9BB 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBC4FF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
51 #B5AFFF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFA399 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBC4FF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
54 #9F8CFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FF674E 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBC4FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
57 #8E74FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FF674D 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBC4FF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
60 #8E73FF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FF5C42 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBC4FF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
63 #8B70FF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FF5C42 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBC4FF 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
66 #8B70FF 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
67 #BBCBFF 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBC4FF 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBBDFF 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
70 #BBCBFF 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBC4FF 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBBDFF 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
73 #BBCBFF 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBC4FF 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBBDFF 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
76 #BBCBFF 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBC4FF 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBBDFF 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
79 #BBCBFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBC4FF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBBDFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFC1BB 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBC4FF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
84 #ADA2FF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FF735B 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBC4FF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
87 #9077FF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FF5137 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBC4FF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
90 #876BFF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FF482E 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBC4FF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
93 #8568FF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FF442B 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBC4FF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
96 #8467FF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBC4FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #ADA2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC8BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
102 #ADA2FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC8BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBC4FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
105 #ADA2FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBC4FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
108 #ADA2FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC8BB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC4FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
111 #ADA1FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC5BB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC4FF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
114 #A899FF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FF9180 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC4FF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
117 #886CFF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FF3C24 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC4FF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
120 #6D4DFF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FF2714 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC4FF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
123 #6849FF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FF0000 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBC4FF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
126 #6444FF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FF0000 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBC4FF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
129 #6444FF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC8BB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBC4FF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
132 #ACA1FF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC9BB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBC4FF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
135 #ACA1FF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFCABB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBC4FF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
138 #ACA1FF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
139 #BBCCFF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBC3FF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
141 #ACA1FF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
142 #BBCBFF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBC3FF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
144 #ACA0FF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFC9BB 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBC2FF 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
147 #9A84FF 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFBABA 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBC0FF 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
150 #7657FF 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FF9485 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBBFFF 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
153 #593AFF 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFA298 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
155 #BBBFFF 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
156 #593AFF 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFA49A 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
158 #BBBEFF 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
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448 #BBC8FF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
449 #AA9DFF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
450 #2311FF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
451 #AFA6FF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
452 #A595FF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
453 #9178FF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
454 #ACA1FF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
455 #A291FF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
456 #9076FF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
457 #A799FF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
458 #A08CFF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
459 #9076FF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
460 #A697FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
461 #9D89FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
462 #8F75FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
463 #A696FF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
464 #9B85FF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
465 #8F75FF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
466 #AEA3FF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
467 #9A85FF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
468 #7455FF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
469 #BBBCFF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
470 #9A85FF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
471 #3A21FF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
472 #BBBFFF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
473 #9A85FF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
474 #1709FF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
475 #BBC0FF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
476 #9A85FF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
477 #0401FF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
478 #BBC0FF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
479 #9A85FF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
480 #0000FF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
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240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
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247 NA NA NA
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249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
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290 NA NA NA
291 NA NA NA
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295 NA NA NA
296 NA NA NA
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299 NA NA NA
300 NA NA NA
301 NA NA NA
302 NA NA NA
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305 NA NA NA
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309 NA NA NA
310 NA NA NA
311 NA NA NA
312 NA NA NA
313 NA NA NA
314 NA NA NA
315 NA NA NA
316 NA NA NA
317 NA NA NA
318 NA NA NA
319 NA NA NA
320 NA NA NA
321 NA NA NA
322 NA NA NA
323 NA NA NA
324 NA NA NA
325 NA NA NA
326 NA NA NA
327 NA NA NA
328 NA NA NA
329 NA NA NA
330 NA NA NA
331 NA NA NA
332 NA NA NA
333 NA NA NA
334 NA NA NA
335 NA NA NA
336 NA NA NA
337 NA NA NA
338 NA NA NA
339 NA NA NA
340 NA NA NA
341 NA NA NA
342 NA NA NA
343 NA NA NA
344 NA NA NA
345 NA NA NA
346 NA NA NA
347 NA NA NA
348 NA NA NA
349 NA NA NA
350 NA NA NA
351 NA NA NA
352 NA NA NA
353 NA NA NA
354 NA NA NA
355 NA NA NA
356 NA NA NA
357 NA NA NA
358 NA NA NA
359 NA NA NA
360 NA NA NA
361 NA NA NA
362 NA NA NA
363 NA NA NA
364 NA NA NA
365 NA NA NA
366 NA NA NA
367 NA NA NA
368 NA NA NA
369 NA NA NA
370 NA NA NA
371 NA NA NA
372 NA NA NA
373 NA NA NA
374 NA NA NA
375 NA NA NA
376 NA NA NA
377 NA NA NA
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379 NA NA NA
380 NA NA NA
381 NA NA NA
382 NA NA NA
383 NA NA NA
384 NA NA NA
385 NA NA NA
386 NA NA NA
387 NA NA NA
388 NA NA NA
389 NA NA NA
390 NA NA NA
391 NA NA NA
392 NA NA NA
393 NA NA NA
394 NA NA NA
395 NA NA NA
396 NA NA NA
397 NA NA NA
398 NA NA NA
399 NA NA NA
400 NA NA NA
401 NA NA NA
402 NA NA NA
403 NA NA NA
404 NA NA NA
405 NA NA NA
406 NA NA NA
407 NA NA NA
408 NA NA NA
409 NA NA NA
410 NA NA NA
411 NA NA NA
412 NA NA NA
413 NA NA NA
414 NA NA NA
415 NA NA NA
416 NA NA NA
417 NA NA NA
418 NA NA NA
419 NA NA NA
420 NA NA NA
421 NA NA NA
422 NA NA NA
423 NA NA NA
424 NA NA NA
425 NA NA NA
426 NA NA NA
427 NA NA NA
428 NA NA NA
429 NA NA NA
430 NA NA NA
431 NA NA NA
432 NA NA NA
433 NA NA NA
434 NA NA NA
435 NA NA NA
436 NA NA NA
437 NA NA NA
438 NA NA NA
439 NA NA NA
440 NA NA NA
441 NA NA NA
442 NA NA NA
443 NA NA NA
444 NA NA NA
445 NA NA NA
446 NA NA NA
447 NA NA NA
448 NA NA NA
449 NA NA NA
450 NA NA NA
451 NA NA NA
452 NA NA NA
453 NA NA NA
454 NA NA NA
455 NA NA NA
456 NA NA NA
457 NA NA NA
458 NA NA NA
459 NA NA NA
460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
$Asia$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #BBC8FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC7FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC5FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFCABB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC7FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC1FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC9BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC7FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC1FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC8BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC7FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC1FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC8BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC7FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC1FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #BBC8FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBC7FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC5FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #BBC9FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBC7FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC4FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFCABB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBC7FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC1FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFC9BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBC7FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC1FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFC8BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBC7FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC1FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBC7FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC1FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
70 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
76 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #BBC8FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBC7FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC5FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #BBCBFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBC7FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC2FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFCABB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBC7FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC1FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFC8BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBC7FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC0FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFC8BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBC7FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC0FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #BBC8FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC7FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC5FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC9BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC7FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC0FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC8BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC7FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC0FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC7BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBC7FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC0FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC7BB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBC7FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC0FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC7BB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBC7FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC0FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC7BB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBC7FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC0FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC7BB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBC7FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC0FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC7BB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBC7FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC0FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC7BB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBC7FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC0FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFC0BB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBC7FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBBEFF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFACA5 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBC7FF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
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$Asia$d2$`gear:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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20 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #BBC4FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC3FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
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29 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
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37 #FFBEBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #BBBFFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #BBBCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #B8B5FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #BBC1FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #BBBDFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #B7B4FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
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11 NA NA
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$Asia$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #B9B7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #B8B5FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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17 #B6B2FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #B4ADFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FF0000 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #B7B3FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #B4AEFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #BAB9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #B6B1FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #B3ACFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
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37 #B9B8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #B7B3FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #B5AFFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #B3ADFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #ADA2FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #A595FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #B3ACFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #ACA0FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #A392FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FF0301 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
47 #B7B3FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
48 #B4AEFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
49 #B5B0FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
50 #ADA2FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
51 #A595FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
52 #B3ACFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
53 #ACA0FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
54 #A392FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
55 #FF0301 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
56 #B7B3FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
57 #B4AEFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
58 #B5B0FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
59 #ADA2FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
60 #A595FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
61 #B1A9FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
62 #ACA0FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
63 #A392FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
64 #B9B7FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
65 #B7B3FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
66 #B5AFFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
67 #B2ABFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
68 #ADA2FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
69 #A596FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
70 #B1A8FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
71 #ACA0FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
72 #A392FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
73 #FF0301 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
74 #B7B3FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
75 #B4AEFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
76 #B5B0FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
77 #ADA2FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
78 #A595FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
79 #B3ACFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
80 #ACA0FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
81 #A392FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
82 #FF0301 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
83 #B7B3FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
84 #B4AEFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
85 #B5B0FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
86 #ADA2FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
87 #A595FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
88 #B3ACFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
89 #ACA0FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
90 #A392FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Asia$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF4C32 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF755E 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCCFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FF1608 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCCFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF0000 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCCFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FF9A8D 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FFC2BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF9A8D 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #BBC9FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FF9A8D 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBC9FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FFB4B1 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #BBCCFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #BBCBFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBC9FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FFC9BB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #BBCBFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #BBC8FF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FFC9BB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #BBCBFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBC8FF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #BBCCFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #FFC0BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #BBCBFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #BBC9FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #BBCCFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #BBCAFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #BBC8FF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #BBCCFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #BBCAFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #BBC8FF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FFC5BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #BBCBFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #BBCBFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #BBCAFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #BBC8FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #BBCCFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #BBCAFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #BBC8FF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #BBCCFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #BBCAFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #BBC8FF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #BBCCFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FFC0BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #BBCBFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBC8FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #BBCBFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #BBCAFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #BBC8FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #BBCCFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #BBCAFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #BBC8FF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #BBCCFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #BBCAFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #BBC8FF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FFC0BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #BBCBFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #BBC8FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #BBCCFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #BBCAFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #BBC8FF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #BBCCFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #BBCAFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #BBC8FF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FFC6BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBC9FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #BBCBFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #BBCAFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #BBC9FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #BBCCFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #BBCAFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #BBC8FF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #BBCCFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #BBCAFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #BBC8FF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #BBCCFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FFC0BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #BBCBFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #BBCBFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #BBCAFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #BBC8FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #BBCCFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #BBCAFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #BBC8FF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #BBCCFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #BBCAFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #BBC8FF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #BBCCFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FFC0BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #BBCBFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #BBCBFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #BBCAFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #BBC8FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #BBCCFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #BBCAFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #BBC8FF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #BBCCFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #BBCAFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #BBC8FF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Asia$eff
$Asia$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 1.197341e-14
2 2 1 2 NA lightgray 0.5 1 NA 1.197341e-14
3 3 1 3 NA lightgray 0.5 1 NA 1.197341e-14
4 4 1 4 NA lightgray 0.5 1 NA 1.197341e-14
5 5 1 5 NA lightgray 0.5 1 NA 1.197341e-14
6 6 1 6 NA lightgray 0.5 1 NA 1.197341e-14
7 7 1 7 NA lightgray 0.5 1 NA 1.197341e-14
xmax ymin ymax
1 1.19744e-14 -Inf Inf
2 1.19744e-14 -Inf Inf
3 1.19744e-14 -Inf Inf
4 1.19744e-14 -Inf Inf
5 1.19744e-14 -Inf Inf
6 1.19744e-14 -Inf Inf
7 1.19744e-14 -Inf Inf
$Asia$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 1.197411e-14 1.197411e-14 1 1 1 0.875 1.125 black 0.5
2 1.197411e-14 1.197411e-14 2 1 2 1.875 2.125 black 0.5
3 -1.178021e-05 3.193949e-05 3 1 3 2.875 3.125 black 0.5
4 -1.894153e-03 5.489234e-03 4 1 4 3.875 4.125 black 0.5
5 -8.380929e-03 3.090750e-02 5 1 5 4.875 5.125 black 0.5
6 -2.322303e-01 1.187455e-01 6 1 6 5.875 6.125 black 0.5
7 -1.939198e+00 NA 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$Asia$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 1.197390e-14 1.197390e-14 0.7 1.3 1 1 1 NA white 0.5
2 1.197390e-14 1.197390e-14 1.7 2.3 2 1 2 NA white 0.5
3 -5.907412e-06 5.907412e-06 2.7 3.3 3 1 3 NA white 0.5
4 -8.579039e-04 8.579039e-04 3.7 4.3 4 1 4 NA white 0.5
5 -1.087804e-02 1.087804e-02 4.7 5.3 5 1 5 NA white 0.5
6 -7.015474e-02 7.015474e-02 5.7 6.3 6 1 6 NA white 0.5
7 -5.844910e-01 5.844910e-01 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$Asia$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.19739e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 1.19739e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 1.19739e-14 NALED 47.8% 3 1 3 black 3 0 0.5 -1 NA
4 1.19739e-14 NALED 40.9% 4 1 4 black 3 0 0.5 -1 NA
5 1.19739e-14 NALED 38.9% 5 1 5 black 3 0 0.5 -1 NA
6 1.19739e-14 NALED 37.7% 6 1 6 black 3 0 0.5 -1 NA
7 1.19739e-14 NALED 43.0% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.197390e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.197390e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -5.907412e-06 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -8.579039e-04 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.087804e-02 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -7.015474e-02 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 -5.844910e-01 ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.197390e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.197390e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 5.907412e-06 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 8.579039e-04 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.087804e-02 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 7.015474e-02 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 5.844910e-01 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.19739e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 1.19739e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 1.19739e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 1.19739e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 1.19739e-14 ALED 0.02 5 1 5 black 3 0 0.5 2 NA
6 1.19739e-14 ALED 0.14 6 1 6 black 3 0 0.5 2 NA
7 1.19739e-14 ALED 1.17 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 1 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$Europe
$Europe$d1
$Europe$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.305116e-14 1.305215e-14
2 -Inf Inf 1.305116e-14 1.305215e-14
3 -Inf Inf 1.305116e-14 1.305215e-14
4 -Inf Inf 1.305116e-14 1.305215e-14
5 -Inf Inf 1.305116e-14 1.305215e-14
6 -Inf Inf 1.305116e-14 1.305215e-14
7 -Inf Inf 1.305116e-14 1.305215e-14
8 -Inf Inf 1.305116e-14 1.305215e-14
9 -Inf Inf 1.305116e-14 1.305215e-14
10 -Inf Inf 1.305116e-14 1.305215e-14
$Europe$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
$Europe$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 -0.00618887 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 -0.01548244 1 2 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.305116e-14 1.305215e-14
2 1.305116e-14 1.305215e-14
$Europe$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.21712565 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.26805387 1 2 NA lightgray 0.5 1 NA -Inf
3 3 0.93059625 1 3 NA lightgray 0.5 1 NA -Inf
4 4 0.76392958 1 4 NA lightgray 0.5 1 NA -Inf
5 5 -0.06882970 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.09783696 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.09783696 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.59590470 1 8 NA lightgray 0.5 1 NA -Inf
9 9 -0.23738828 1 9 NA lightgray 0.5 1 NA -Inf
10 10 -0.08414960 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.24918374 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.41585053 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.41585053 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.25025654 1 14 NA lightgray 0.5 1 NA -Inf
15 15 0.75025654 1 15 NA lightgray 0.5 1 NA -Inf
16 16 0.44517303 1 16 NA lightgray 0.5 1 NA -Inf
17 17 1.32301938 1 17 NA lightgray 0.5 1 NA -Inf
18 18 1.15711598 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.99044927 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.95687941 1 20 NA lightgray 0.5 1 NA -Inf
21 21 1.13019402 1 21 NA lightgray 0.5 1 NA -Inf
22 22 1.63702005 1 22 NA lightgray 0.5 1 NA -Inf
23 23 1.30593413 1 23 NA lightgray 0.5 1 NA -Inf
24 24 1.97260071 1 24 NA lightgray 0.5 1 NA -Inf
25 25 1.97260070 1 25 NA lightgray 0.5 1 NA -Inf
26 26 2.30593426 1 26 NA lightgray 0.5 1 NA -Inf
27 27 2.63319411 1 27 NA lightgray 0.5 1 NA -Inf
28 28 2.07246592 1 28 NA lightgray 0.5 1 NA -Inf
29 29 2.57245050 1 29 NA lightgray 0.5 1 NA -Inf
30 30 2.23178490 1 30 NA lightgray 0.5 1 NA -Inf
31 31 1.89843292 1 31 NA lightgray 0.5 1 NA -Inf
32 32 2.56509702 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
3 Inf 1.305116e-14 1.305215e-14
4 Inf 1.305116e-14 1.305215e-14
5 Inf 1.305116e-14 1.305215e-14
6 Inf 1.305116e-14 1.305215e-14
7 Inf 1.305116e-14 1.305215e-14
8 Inf 1.305116e-14 1.305215e-14
9 Inf 1.305116e-14 1.305215e-14
10 Inf 1.305116e-14 1.305215e-14
11 Inf 1.305116e-14 1.305215e-14
12 Inf 1.305116e-14 1.305215e-14
13 Inf 1.305116e-14 1.305215e-14
14 Inf 1.305116e-14 1.305215e-14
15 Inf 1.305116e-14 1.305215e-14
16 Inf 1.305116e-14 1.305215e-14
17 Inf 1.305116e-14 1.305215e-14
18 Inf 1.305116e-14 1.305215e-14
19 Inf 1.305116e-14 1.305215e-14
20 Inf 1.305116e-14 1.305215e-14
21 Inf 1.305116e-14 1.305215e-14
22 Inf 1.305116e-14 1.305215e-14
23 Inf 1.305116e-14 1.305215e-14
24 Inf 1.305116e-14 1.305215e-14
25 Inf 1.305116e-14 1.305215e-14
26 Inf 1.305116e-14 1.305215e-14
27 Inf 1.305116e-14 1.305215e-14
28 Inf 1.305116e-14 1.305215e-14
29 Inf 1.305116e-14 1.305215e-14
30 Inf 1.305116e-14 1.305215e-14
31 Inf 1.305116e-14 1.305215e-14
32 Inf 1.305116e-14 1.305215e-14
$Europe$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 -0.2389154 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 0.2471940 1 2 NA lightgray 0.5 1 NA -Inf Inf
3 3 0.3799475 1 3 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.305116e-14 1.305215e-14
2 1.305116e-14 1.305215e-14
3 1.305116e-14 1.305215e-14
$Europe$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.005489341 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 0.001898392 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 0.001898401 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 0.001898401 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 0.001898406 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
3 Inf 1.305116e-14 1.305215e-14
4 Inf 1.305116e-14 1.305215e-14
5 Inf 1.305116e-14 1.305215e-14
$Europe$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 -3.194015e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 1.434104e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 9.061769e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.305116e-14 1.305215e-14
2 -Inf Inf 1.305116e-14 1.305215e-14
3 -Inf Inf 1.305116e-14 1.305215e-14
4 -Inf Inf 1.305116e-14 1.305215e-14
5 -Inf Inf 1.305116e-14 1.305215e-14
6 -Inf Inf 1.305116e-14 1.305215e-14
7 -Inf Inf 1.305116e-14 1.305215e-14
8 -Inf Inf 1.305116e-14 1.305215e-14
9 -Inf Inf 1.305116e-14 1.305215e-14
10 -Inf Inf 1.305116e-14 1.305215e-14
$Europe$d2
$Europe$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
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45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
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47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
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62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
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80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
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89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
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95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
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98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
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123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
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185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
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188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
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192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
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194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
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200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
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203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
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206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
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209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
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215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
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218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
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227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
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231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
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236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
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239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
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245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
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248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
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252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
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254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
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257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
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260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
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263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
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266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
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269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
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276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
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279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
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285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
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288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
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291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
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293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
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296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
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305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
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309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
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312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
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315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
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317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
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320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
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327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
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330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
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444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
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507 #FFBBBB 9 17 1 273 8.5 9.5 16.5 17.5 NA 0.1 1
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9 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
64 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
65 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
66 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
67 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
68 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
69 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
70 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
71 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
72 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
73 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
77 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
78 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
80 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
81 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
82 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
83 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
84 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
85 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
86 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
87 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
88 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
89 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
90 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Europe$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Europe$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
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6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
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8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
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21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
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23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
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35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
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38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
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66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
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293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
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$Europe$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
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width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
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6 NA NA
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10 NA NA
11 NA NA
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$Europe$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
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2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
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6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
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12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
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16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
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18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
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31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
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37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
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40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
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42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
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46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
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48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
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51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
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55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
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63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
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85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
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91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
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97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
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alpha width height
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48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Europe$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Europe$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Europe$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF8F7F 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBBFFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC5BB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFC8BB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFCBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF8E7D 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #BBC0FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBFBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFC6BB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFCBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FF8E7D 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #B5B0FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBFBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFCBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #BBC6FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FF8E7D 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFCBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #B5B0FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBFBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFCBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #BBC6FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FF8E7D 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFCBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #B5B0FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBFBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFCBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #BBC6FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FF8E7D 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFCBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #B6B1FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFC0BB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFCBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #BBC6FF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FF8E7D 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFCBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #B6B1FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFC0BB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFCBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #BBC6FF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FF8D7C 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFCBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #B5AFFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBFBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFCBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #BBC2FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FF6950 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFCBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #A89AFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBFBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFCBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #BBC4FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FF7C66 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFCBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #ADA3FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBFBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFCBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #BBC3FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FF7C66 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFCBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #ADA3FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBFBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFCBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #BBC5FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FF8E7D 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFCBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #B3ACFF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBFBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFCBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #BBC5FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FF8E7D 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #BBCBFF 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #A595FF 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBFBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #BBCBFF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #BBC0FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FF8E7D 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #BBCBFF 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #A595FF 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBFBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #BBCBFF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #BBC0FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FF8E7D 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #BBCBFF 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #A595FF 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBFBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #BBCBFF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #BBC0FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FF8E7D 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #BBCBFF 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #A595FF 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFC0BB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #BBCAFF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #BBC0FF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FF8E7D 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #BBCBFF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #A697FF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFC0BB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #BBCAFF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #BBC0FF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FF8D7C 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #BBCBFF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #A697FF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFADA7 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #BBCAFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #BAB9FF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FF6950 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #BBCBFF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #9881FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFABA4 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #BBC8FF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #A08DFF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FF6950 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #BBCBFF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #9881FF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFC0BB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #BBC7FF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #A798FF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FF7C66 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #BBCBFF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #9F8CFF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
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126 #A290FF 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FF7E69 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #BBC7FF 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #8D73FF 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFB7B6 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #BBC3FF 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #8C71FF 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FF9181 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #BBC5FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #8B70FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FF765E 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #BBC3FF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #6C4DFF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FF9181 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #BBC5FF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #8B70FF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FF3C24 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #BBC3FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #5A3CFF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FF9383 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #BBC4FF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #8A6FFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FF0000 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #BBC1FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #4D30FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FF9383 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #BBC4FF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #8A6FFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FF0D04 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #BBBFFF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #4B2EFF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FF9383 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #BBC4FF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #8A6FFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FF553B 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #BBBCFF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #1609FF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FF9383 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #BBC4FF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #8C71FF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FF5339 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #BBBDFF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #482CFF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FF9282 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #BBC4FF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #8B70FF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FF0903 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #BBBDFF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #0000FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FF6F56 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #BBC4FF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #785AFF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FF0903 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #BBBDFF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #0000FF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FF816C 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #BBC4FF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #8265FF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FF3A23 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #BBBDFF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #321CFF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FF816C 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #BBC4FF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #8265FF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FF3A23 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #BBBDFF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #321CFF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
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93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
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100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
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110 NA NA NA
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112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Europe$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FF563C 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #B7B3FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF8875 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC8BB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #B3ACFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FF0000 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFC1BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #B8B5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF5E44 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBABA 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC1FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FF0000 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFC1BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #B8B5FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF8672 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #BBCAFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #9178FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Europe$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFCABB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFB8B7 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFCCBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFC1BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFC6BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #BBCCFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFB4B1 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFC2BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FF452C 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FF6A51 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FF8B79 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFB4B2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF0000 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FF6A51 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FF8D7B 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Europe$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBFBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFC7BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFB3B0 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFC7BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFB1AD 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFC0BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFA196 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFAEA8 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBDBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FF8A78 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFB6B4 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF5F45 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FF674E 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FF6C53 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FF8976 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFB6B4 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FF3A22 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FF573D 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FF6950 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FF8976 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFB6B4 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FF301A 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FF4F35 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FF684F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FF8A78 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFB6B4 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFC0BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FF331D 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FF4F35 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FF684F 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FF8976 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFB6B4 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FF4F35 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FF6950 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FF8976 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFB6B4 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FF4F35 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FF6950 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC1BB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC1BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC5FF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC1BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCAFF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC5FF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF998C 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCCFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC8FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBEBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCCFF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC8FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFAAA2 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCCFF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC8FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFAAA2 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCCFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC8FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCCFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC8FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBEBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCBFF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC7FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBEBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCBFF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC8FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FF6F57 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCBFF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
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178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$Europe$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC4BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFC6BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFCABB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBFBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFC6BB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC3FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBCBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFC6BB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #B4AEFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBCBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFC6BB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #B4AEFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFC6BB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #AFA6FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFC6BB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #AFA6FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC3BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFC6BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #FFCABB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFC6BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #FFCABB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFC6BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #FFCABB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFC6BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
45 #FFCABB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFC6BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
48 #FFCABB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC0BB 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFC6BB 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC5FF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFB7B6 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFC6BB 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
54 #A18EFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFB1AD 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFC6BB 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
57 #9076FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFB0AB 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFC6BB 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
60 #8B70FF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFAFAA 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFC6BB 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
63 #896EFF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC2BB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFC6BB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
66 #FFCBBB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC2BB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFC6BB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
69 #FFCBBB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC2BB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFC6BB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
72 #FFCBBB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC2BB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFC6BB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
75 #FFCBBB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC2BB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFC6BB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
78 #FFCBBB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFC1BB 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFC6BB 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBCBFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFBCBB 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFC6BB 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
84 #B3ACFF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFB0AB 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFC6BB 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
87 #8A6EFF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFAEA8 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFC6BB 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
90 #8265FF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFACA5 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFC6BB 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
93 #7B5DFF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFACA5 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFC6BB 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
96 #7B5DFF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFC0BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC0BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
101 #FFC6BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC1FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC0BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFC6BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC1FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC0BB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFC6BB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC1FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC0BB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
110 #FFC6BB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC1FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFBEBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
113 #FFC6BB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
114 #AB9FFF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFB5B2 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
116 #FFC6BB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
117 #7556FF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFA8A0 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
119 #FFC6BB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
120 #2A16FF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFA8A0 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
122 #FFC6BB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
123 #2915FF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFA69D 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFC6BB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
126 #0000FF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFA69D 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFC6BB 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
129 #0000FF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC0BB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFC6BB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC1FF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC0BB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFC6BB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC1FF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC0BB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFC6BB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC1FF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFBFBB 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFC6BB 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC1FF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFBFBB 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFC5BB 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC1FF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFB9B8 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFC5BB 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
147 #A696FF 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FF9F93 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFC4BB 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
150 #6F50FF 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FF9585 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFC3BB 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
153 #5738FF 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FF907F 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC3BB 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
156 #4E31FF 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FF8D7B 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC3BB 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
159 #4B2EFF 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFBFBB 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC2BB 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
162 #FFC4BB 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFBFBB 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC2BB 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
165 #FFC4BB 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFBFBB 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC2BB 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
168 #FFC4BB 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFBFBB 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC2BB 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
171 #FFC4BB 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFBFBB 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC2BB 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
174 #FFC4BB 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FFBEBB 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC2BB 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
177 #FFC5BB 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FFB4B1 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC2BB 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
180 #FFCBBB 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FFA59B 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC2BB 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
183 #BBC1FF 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FFA298 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC2BB 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
186 #BBBFFF 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FFA095 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC2BB 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
189 #BBBDFF 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FFA095 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC2BB 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
192 #BBBDFF 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFC3BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFC3BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
197 #FFC6BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
198 #FFCABB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC3BB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFC6BB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
201 #FFCABB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC3BB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFC6BB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
204 #FFCABB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFC3BB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
206 #FFC6BB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
207 #FFCABB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFC0BB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
209 #FFC6BB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
210 #BBC7FF 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFBCBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
212 #FFC6BB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
213 #B2ABFF 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFB1AD 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
215 #FFC6BB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
216 #9279FF 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFB1AC 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
218 #FFC6BB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
219 #9279FF 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFAFAA 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFC6BB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
222 #8C71FF 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFAFA9 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFC6BB 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
225 #8C71FF 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC1BB 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFC6BB 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
228 #FFCBBB 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC1BB 13 3 1 63 12.5 13.5 2.5 3.5 NA 0.1 1
230 #FFC6BB 13 3 1 63 12.5 13.5 2.5 3.5 NA 0.1 1
231 #FFCBBB 13 3 1 63 12.5 13.5 2.5 3.5 NA 0.1 1
232 #FFC1BB 14 3 1 68 13.5 14.5 2.5 3.5 NA 0.1 1
233 #FFC5BB 14 3 1 68 13.5 14.5 2.5 3.5 NA 0.1 1
234 #FFCBBB 14 3 1 68 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFBFBB 15 3 1 73 14.5 15.5 2.5 3.5 NA 0.1 1
236 #FFC5BB 15 3 1 73 14.5 15.5 2.5 3.5 NA 0.1 1
237 #FFCBBB 15 3 1 73 14.5 15.5 2.5 3.5 NA 0.1 1
238 #FFBFBB 16 3 1 78 15.5 16.5 2.5 3.5 NA 0.1 1
239 #FFC5BB 16 3 1 78 15.5 16.5 2.5 3.5 NA 0.1 1
240 #FFCBBB 16 3 1 78 15.5 16.5 2.5 3.5 NA 0.1 1
241 #FFB9B7 17 3 1 83 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC4BB 17 3 1 83 16.5 17.5 2.5 3.5 NA 0.1 1
243 #BBC3FF 17 3 1 83 16.5 17.5 2.5 3.5 NA 0.1 1
244 #FF9D91 18 3 1 88 17.5 18.5 2.5 3.5 NA 0.1 1
245 #FFC2BB 18 3 1 88 17.5 18.5 2.5 3.5 NA 0.1 1
246 #A89AFF 18 3 1 88 17.5 18.5 2.5 3.5 NA 0.1 1
247 #FF907F 19 3 1 93 18.5 19.5 2.5 3.5 NA 0.1 1
248 #FFC1BB 19 3 1 93 18.5 19.5 2.5 3.5 NA 0.1 1
249 #9B86FF 19 3 1 93 18.5 19.5 2.5 3.5 NA 0.1 1
250 #FF8A78 20 3 1 98 19.5 20.5 2.5 3.5 NA 0.1 1
251 #FFC1BB 20 3 1 98 19.5 20.5 2.5 3.5 NA 0.1 1
252 #9780FF 20 3 1 98 19.5 20.5 2.5 3.5 NA 0.1 1
253 #FF8571 21 3 1 103 20.5 21.5 2.5 3.5 NA 0.1 1
254 #FFC0BB 21 3 1 103 20.5 21.5 2.5 3.5 NA 0.1 1
255 #9D89FF 21 3 1 103 20.5 21.5 2.5 3.5 NA 0.1 1
256 #FFA59C 22 3 1 108 21.5 22.5 2.5 3.5 NA 0.1 1
257 #FFBFBB 22 3 1 108 21.5 22.5 2.5 3.5 NA 0.1 1
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303 NA NA NA
304 NA NA NA
305 NA NA NA
306 NA NA NA
307 NA NA NA
308 NA NA NA
309 NA NA NA
310 NA NA NA
311 NA NA NA
312 NA NA NA
313 NA NA NA
314 NA NA NA
315 NA NA NA
316 NA NA NA
317 NA NA NA
318 NA NA NA
319 NA NA NA
320 NA NA NA
321 NA NA NA
322 NA NA NA
323 NA NA NA
324 NA NA NA
325 NA NA NA
326 NA NA NA
327 NA NA NA
328 NA NA NA
329 NA NA NA
330 NA NA NA
331 NA NA NA
332 NA NA NA
333 NA NA NA
334 NA NA NA
335 NA NA NA
336 NA NA NA
337 NA NA NA
338 NA NA NA
339 NA NA NA
340 NA NA NA
341 NA NA NA
342 NA NA NA
343 NA NA NA
344 NA NA NA
345 NA NA NA
346 NA NA NA
347 NA NA NA
348 NA NA NA
349 NA NA NA
350 NA NA NA
351 NA NA NA
352 NA NA NA
353 NA NA NA
354 NA NA NA
355 NA NA NA
356 NA NA NA
357 NA NA NA
358 NA NA NA
359 NA NA NA
360 NA NA NA
361 NA NA NA
362 NA NA NA
363 NA NA NA
364 NA NA NA
365 NA NA NA
366 NA NA NA
367 NA NA NA
368 NA NA NA
369 NA NA NA
370 NA NA NA
371 NA NA NA
372 NA NA NA
373 NA NA NA
374 NA NA NA
375 NA NA NA
376 NA NA NA
377 NA NA NA
378 NA NA NA
379 NA NA NA
380 NA NA NA
381 NA NA NA
382 NA NA NA
383 NA NA NA
384 NA NA NA
385 NA NA NA
386 NA NA NA
387 NA NA NA
388 NA NA NA
389 NA NA NA
390 NA NA NA
391 NA NA NA
392 NA NA NA
393 NA NA NA
394 NA NA NA
395 NA NA NA
396 NA NA NA
397 NA NA NA
398 NA NA NA
399 NA NA NA
400 NA NA NA
401 NA NA NA
402 NA NA NA
403 NA NA NA
404 NA NA NA
405 NA NA NA
406 NA NA NA
407 NA NA NA
408 NA NA NA
409 NA NA NA
410 NA NA NA
411 NA NA NA
412 NA NA NA
413 NA NA NA
414 NA NA NA
415 NA NA NA
416 NA NA NA
417 NA NA NA
418 NA NA NA
419 NA NA NA
420 NA NA NA
421 NA NA NA
422 NA NA NA
423 NA NA NA
424 NA NA NA
425 NA NA NA
426 NA NA NA
427 NA NA NA
428 NA NA NA
429 NA NA NA
430 NA NA NA
431 NA NA NA
432 NA NA NA
433 NA NA NA
434 NA NA NA
435 NA NA NA
436 NA NA NA
437 NA NA NA
438 NA NA NA
439 NA NA NA
440 NA NA NA
441 NA NA NA
442 NA NA NA
443 NA NA NA
444 NA NA NA
445 NA NA NA
446 NA NA NA
447 NA NA NA
448 NA NA NA
449 NA NA NA
450 NA NA NA
451 NA NA NA
452 NA NA NA
453 NA NA NA
454 NA NA NA
455 NA NA NA
456 NA NA NA
457 NA NA NA
458 NA NA NA
459 NA NA NA
460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
$Europe$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFC8BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFC9BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC7BB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFC8BB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFCABB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC5BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFC8BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBCCFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC5BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFC8BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBCBFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC5BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFC8BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBCBFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC5BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFC8BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBCBFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC8BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFC8BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #FFC9BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFC5BB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFC8BB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #FFCCBB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC5BB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFC8BB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBCCFF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFC5BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFC8BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBCBFF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFC5BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFC8BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBCBFF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
69 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
75 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
78 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFC8BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFC8BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #FFC9BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFC5BB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFC8BB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBCCFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFC5BB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFC8BB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBCBFF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFC4BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFC8BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBCBFF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFC4BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFC8BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBCBFF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #FFC8BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #FFC9BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC7BB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #FFC8BB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #FFCABB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC5BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #FFC8BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBCBFF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC4BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #FFC8BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBCAFF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC4BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFC8BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBCAFF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC4BB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFC8BB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBCAFF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC4BB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFC8BB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC9FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC4BB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFC8BB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC9FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC4BB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFC8BB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC9FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC4BB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFC8BB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC9FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC4BB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFC8BB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC9FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFC4BB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFC8BB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBC4FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFC1BB 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFC8BB 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
150 #B2AAFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC1BB 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFC8BB 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
153 #A99BFF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFC1BB 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC8BB 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
156 #A797FF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFC1BB 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC8BB 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
159 #A797FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFC4BB 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC8BB 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBC7FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFC4BB 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC8BB 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBC7FF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFC4BB 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC8BB 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBC7FF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFC4BB 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC8BB 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
171 #BBC7FF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFC4BB 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC8BB 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
174 #BBC7FF 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FFC4BB 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC8BB 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
177 #BBC6FF 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FFC3BB 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC8BB 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
180 #BBC2FF 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FFC0BB 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC8BB 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
183 #A493FF 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FFC0BB 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC8BB 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
186 #A290FF 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FFC0BB 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC8BB 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
189 #A18EFF 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FFC0BB 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC8BB 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
192 #A18EFF 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFC4BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFC4BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
197 #FFC8BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBC6FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC4BB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFC8BB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBC6FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC4BB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFC8BB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBC6FF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFC4BB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
206 #FFC8BB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
207 #BBC6FF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFC3BB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
209 #FFC8BB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
210 #BBC1FF 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFC2BB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
212 #FFC8BB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
213 #BBBCFF 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFC0BB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
215 #FFC8BB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
216 #A18FFF 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFC0BB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
218 #FFC8BB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
219 #9F8CFF 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFC0BB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFC8BB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
222 #9E8BFF 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC0BB 11 3 1 103 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFC8BB 11 3 1 103 10.5 11.5 2.5 3.5 NA 0.1 1
225 #9E8BFF 11 3 1 103 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC4BB 12 3 1 113 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFC8BB 12 3 1 113 11.5 12.5 2.5 3.5 NA 0.1 1
228 #BBC6FF 12 3 1 113 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC4BB 13 3 1 123 12.5 13.5 2.5 3.5 NA 0.1 1
230 #FFC8BB 13 3 1 123 12.5 13.5 2.5 3.5 NA 0.1 1
231 #BBC6FF 13 3 1 123 12.5 13.5 2.5 3.5 NA 0.1 1
232 #FFC4BB 14 3 1 133 13.5 14.5 2.5 3.5 NA 0.1 1
233 #FFC8BB 14 3 1 133 13.5 14.5 2.5 3.5 NA 0.1 1
234 #BBC6FF 14 3 1 133 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFC4BB 15 3 1 143 14.5 15.5 2.5 3.5 NA 0.1 1
236 #FFC8BB 15 3 1 143 14.5 15.5 2.5 3.5 NA 0.1 1
237 #BBC6FF 15 3 1 143 14.5 15.5 2.5 3.5 NA 0.1 1
238 #FFC4BB 16 3 1 153 15.5 16.5 2.5 3.5 NA 0.1 1
239 #FFC8BB 16 3 1 153 15.5 16.5 2.5 3.5 NA 0.1 1
240 #BBC6FF 16 3 1 153 15.5 16.5 2.5 3.5 NA 0.1 1
241 #FFC3BB 17 3 1 163 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC8BB 17 3 1 163 16.5 17.5 2.5 3.5 NA 0.1 1
243 #BBC1FF 17 3 1 163 16.5 17.5 2.5 3.5 NA 0.1 1
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1 #FF9C8F 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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22 #FF6950 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
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width height
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$Europe$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
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width height
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$Europe$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF9180 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF816C 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
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10 #FFADA6 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
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18 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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20 #FF9E92 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
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22 #FF4C32 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #FF9E92 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF8875 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #FF9E92 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #FFB0AB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
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29 #FF9E92 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FF836F 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
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44 #FF8C7A 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBCCFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FF836F 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #FFB4B1 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #FF351F 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #FF9282 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBCCFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #FF301B 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #FF8773 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #FFACA5 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #FF6D55 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #FF8773 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #FFA79E 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #FF1F0F 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #FF8773 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #FFB2AE 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FF8976 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #FFB4B1 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FF3F26 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #FF9282 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBCCFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #FF0000 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #FF8673 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #FFB7B5 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #FF664D 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #FF8673 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #FFA79E 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #FF0000 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #FF8673 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #FFB7B5 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FF836F 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #FFB4B1 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FF351F 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #FF9282 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBCCFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #FF0000 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #FF8673 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #FFB7B5 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #FF664D 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #FF8673 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #FFA79E 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #FF0000 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #FF8673 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #FFB7B5 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FF836F 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #FFB4B1 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FF351F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #FF9282 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBCCFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #FF0000 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #FF8673 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #FFB7B5 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #FF745C 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #FF8673 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #FFA69D 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #FF301B 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #FF8673 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #FFABA4 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #FF8A77 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #FFB4B1 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FF4028 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #FF9282 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBCCFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #FF1C0C 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #FF8673 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #FFB2AE 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #FF664D 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #FF8673 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #FFA79E 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #FF0000 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #FF8673 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #FFB7B5 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FF836F 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #FFB4B1 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FF351F 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #FF9282 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBCCFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #FF8673 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #FFB7B5 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #FF664D 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #FF8673 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #FFA79E 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #FF0000 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #FF8673 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #FFB7B5 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FF836F 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #FFB4B1 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FF351F 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #FF9282 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBCCFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #FF8673 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #FFB7B5 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #FF664D 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #FF8673 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #FFA79E 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #FF0000 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #FF8673 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #FFB7B5 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Europe$eff
$Europe$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 1.305116e-14
2 2 1 2 NA lightgray 0.5 1 NA 1.305116e-14
3 3 1 3 NA lightgray 0.5 1 NA 1.305116e-14
4 4 1 4 NA lightgray 0.5 1 NA 1.305116e-14
5 5 1 5 NA lightgray 0.5 1 NA 1.305116e-14
6 6 1 6 NA lightgray 0.5 1 NA 1.305116e-14
7 7 1 7 NA lightgray 0.5 1 NA 1.305116e-14
xmax ymin ymax
1 1.305215e-14 -Inf Inf
2 1.305215e-14 -Inf Inf
3 1.305215e-14 -Inf Inf
4 1.305215e-14 -Inf Inf
5 1.305215e-14 -Inf Inf
6 1.305215e-14 -Inf Inf
7 1.305215e-14 -Inf Inf
$Europe$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 1.305186e-14 1.305186e-14 1 1 1 0.875 1.125 black 0.5
2 1.305186e-14 1.305186e-14 2 1 2 1.875 2.125 black 0.5
3 -3.194015e-05 1.177986e-05 3 1 3 2.875 3.125 black 0.5
4 -5.489341e-03 1.894194e-03 4 1 4 3.875 4.125 black 0.5
5 -3.090750e-02 8.380929e-03 5 1 5 4.875 5.125 black 0.5
6 -1.722488e-01 3.647938e-01 6 1 6 5.875 6.125 black 0.5
7 -6.800969e-01 NA 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$Europe$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 1.305165e-14 1.305165e-14 0.7 1.3 1 1 1 NA white 0.5
2 1.305165e-14 1.305165e-14 1.7 2.3 2 1 2 NA white 0.5
3 -5.907267e-06 5.907267e-06 2.7 3.3 3 1 3 NA white 0.5
4 -8.579205e-04 8.579205e-04 3.7 4.3 4 1 4 NA white 0.5
5 -1.087804e-02 1.087804e-02 4.7 5.3 5 1 5 NA white 0.5
6 -1.157814e-01 1.157814e-01 5.7 6.3 6 1 6 NA white 0.5
7 NA 1.136806e+00 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$Europe$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 1.305165e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 1.305165e-14 NALED 10.7% 3 1 3 black 3 0 0.5 -1 NA
4 1.305165e-14 NALED 27.4% 4 1 4 black 3 0 0.5 -1 NA
5 1.305165e-14 NALED 32.2% 5 1 5 black 3 0 0.5 -1 NA
6 1.305165e-14 NALED 30.2% 6 1 6 black 3 0 0.5 -1 NA
7 1.305165e-14 NALED 45.9% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.305165e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -5.907267e-06 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -8.579205e-04 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.087804e-02 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -1.157814e-01 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 NA ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.305165e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 5.907267e-06 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 8.579205e-04 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.087804e-02 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 1.157814e-01 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 1.136806e+00 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 1.305165e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 1.305165e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 1.305165e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 1.305165e-14 ALED 0.02 5 1 5 black 3 0 0.5 2 NA
6 1.305165e-14 ALED 0.23 6 1 6 black 3 0 0.5 2 NA
7 1.305165e-14 ALED 2.27 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 1 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$`North America`
$`North America`$d1
$`North America`$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 2.637704e-14 2.637802e-14
2 -Inf Inf 2.637704e-14 2.637802e-14
3 -Inf Inf 2.637704e-14 2.637802e-14
4 -Inf Inf 2.637704e-14 2.637802e-14
5 -Inf Inf 2.637704e-14 2.637802e-14
6 -Inf Inf 2.637704e-14 2.637802e-14
7 -Inf Inf 2.637704e-14 2.637802e-14
8 -Inf Inf 2.637704e-14 2.637802e-14
9 -Inf Inf 2.637704e-14 2.637802e-14
10 -Inf Inf 2.637704e-14 2.637802e-14
$`North America`$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 2.637774e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 2.637774e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -5.571100e-10 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.565414e-10 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.19456011 1 1 NA lightgray 0.5 1 NA -Inf
2 2 -0.36122678 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.69534840 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.52868173 1 4 NA lightgray 0.5 1 NA -Inf
5 5 -0.02868173 1 5 NA lightgray 0.5 1 NA -Inf
6 6 -0.36201506 1 6 NA lightgray 0.5 1 NA -Inf
7 7 -0.36201506 1 7 NA lightgray 0.5 1 NA -Inf
8 8 -0.86201506 1 8 NA lightgray 0.5 1 NA -Inf
9 9 -0.19534839 1 9 NA lightgray 0.5 1 NA -Inf
10 10 -0.36201506 1 10 NA lightgray 0.5 1 NA -Inf
11 11 -0.69534839 1 11 NA lightgray 0.5 1 NA -Inf
12 12 -0.86201518 1 12 NA lightgray 0.5 1 NA -Inf
13 13 -0.86201518 1 13 NA lightgray 0.5 1 NA -Inf
14 14 -1.02975453 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.52994744 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.39153060 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -1.26931872 1 17 NA lightgray 0.5 1 NA -Inf
18 18 -1.26931875 1 18 NA lightgray 0.5 1 NA -Inf
19 19 -1.10265208 1 19 NA lightgray 0.5 1 NA -Inf
20 20 -1.10265208 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -1.10265208 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -1.26931874 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -1.26931920 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -1.27395738 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -1.27395738 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -1.60729073 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.60729073 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -1.54656020 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.54656020 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -1.37256419 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -1.37252079 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.53918841 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
3 Inf 2.637704e-14 2.637802e-14
4 Inf 2.637704e-14 2.637802e-14
5 Inf 2.637704e-14 2.637802e-14
6 Inf 2.637704e-14 2.637802e-14
7 Inf 2.637704e-14 2.637802e-14
8 Inf 2.637704e-14 2.637802e-14
9 Inf 2.637704e-14 2.637802e-14
10 Inf 2.637704e-14 2.637802e-14
11 Inf 2.637704e-14 2.637802e-14
12 Inf 2.637704e-14 2.637802e-14
13 Inf 2.637704e-14 2.637802e-14
14 Inf 2.637704e-14 2.637802e-14
15 Inf 2.637704e-14 2.637802e-14
16 Inf 2.637704e-14 2.637802e-14
17 Inf 2.637704e-14 2.637802e-14
18 Inf 2.637704e-14 2.637802e-14
19 Inf 2.637704e-14 2.637802e-14
20 Inf 2.637704e-14 2.637802e-14
21 Inf 2.637704e-14 2.637802e-14
22 Inf 2.637704e-14 2.637802e-14
23 Inf 2.637704e-14 2.637802e-14
24 Inf 2.637704e-14 2.637802e-14
25 Inf 2.637704e-14 2.637802e-14
26 Inf 2.637704e-14 2.637802e-14
27 Inf 2.637704e-14 2.637802e-14
28 Inf 2.637704e-14 2.637802e-14
29 Inf 2.637704e-14 2.637802e-14
30 Inf 2.637704e-14 2.637802e-14
31 Inf 2.637704e-14 2.637802e-14
32 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 0.12047749 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 -0.24916998 1 2 NA lightgray 0.5 1 NA -Inf Inf
3 3 -0.04982882 1 3 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 2.637704e-14 2.637802e-14
2 2.637704e-14 2.637802e-14
3 2.637704e-14 2.637802e-14
$`North America`$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.076012e-07 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -3.593976e-08 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -3.594862e-08 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 -3.594871e-08 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 -4.130466e-08 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
3 Inf 2.637704e-14 2.637802e-14
4 Inf 2.637704e-14 2.637802e-14
5 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 6.601906e-10 1 -1 NA lightgray 0.5 1 NA
2 1.935000 2.636903e-10 1 -1 NA lightgray 0.5 1 NA
3 2.465000 2.186298e-10 1 -1 NA lightgray 0.5 1 NA
4 2.790481 2.186272e-10 1 -1 NA lightgray 0.5 1 NA
5 3.190000 2.186272e-10 1 -1 NA lightgray 0.5 1 NA
6 3.439356 2.193488e-10 1 -1 NA lightgray 0.5 1 NA
7 3.520000 2.194414e-10 1 -1 NA lightgray 0.5 1 NA
8 3.730000 2.194093e-10 1 -1 NA lightgray 0.5 1 NA
9 4.046066 2.194083e-10 1 -1 NA lightgray 0.5 1 NA
10 5.453272 2.194047e-10 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 2.637704e-14 2.637802e-14
2 -Inf Inf 2.637704e-14 2.637802e-14
3 -Inf Inf 2.637704e-14 2.637802e-14
4 -Inf Inf 2.637704e-14 2.637802e-14
5 -Inf Inf 2.637704e-14 2.637802e-14
6 -Inf Inf 2.637704e-14 2.637802e-14
7 -Inf Inf 2.637704e-14 2.637802e-14
8 -Inf Inf 2.637704e-14 2.637802e-14
9 -Inf Inf 2.637704e-14 2.637802e-14
10 -Inf Inf 2.637704e-14 2.637802e-14
$`North America`$d2
$`North America`$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
302 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
303 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
304 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
307 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
313 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
316 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
322 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
331 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
332 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
333 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
334 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
335 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
337 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
340 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
342 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
343 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
344 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
345 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
346 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
347 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
348 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
349 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
350 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
351 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
352 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
353 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
358 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
359 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
360 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
361 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
362 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
363 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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365 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
366 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
367 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
368 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
369 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
370 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
371 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
372 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
373 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
374 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
375 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
376 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
377 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
378 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
379 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
380 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
381 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
382 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
383 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
384 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
385 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
386 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
387 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
388 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
389 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
390 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
391 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
392 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
393 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
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395 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
396 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
397 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
398 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
399 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
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$`North America`$d2$`mpg:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
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24 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
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30 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
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46 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
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48 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
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50 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
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60 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
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63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
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68 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
69 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
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73 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
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78 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
80 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
81 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
82 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
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84 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
85 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
86 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
87 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
88 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
89 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
90 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$`North America`$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$`North America`$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
289 NA NA NA
290 NA NA NA
291 NA NA NA
292 NA NA NA
293 NA NA NA
294 NA NA NA
295 NA NA NA
296 NA NA NA
297 NA NA NA
298 NA NA NA
299 NA NA NA
300 NA NA NA
$`North America`$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
$`North America`$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$`North America`$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$`North America`$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$`North America`$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF6D54 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFCCBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #A798FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FF6D54 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFCCBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #A798FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FF6D54 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFCCBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #A798FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FF6D54 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFCCBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #A798FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FF6D54 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFCCBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #A798FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFCABB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFCCBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBCCFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FF6D54 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFCCBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #A99BFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFCBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFCCBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FF6B52 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFCCBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #A89AFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBFBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC4FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FF2512 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFCCBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #8D72FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBFBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFCCBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC4FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FF4F35 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFCCBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #9B86FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFC5BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFCCBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC9FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FF4F35 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFCCBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #9B86FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC5BB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFCCBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #BBC9FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FF6D54 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFCCBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #A798FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FF6D54 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFCCBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #A798FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FF6D54 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFCCBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #A798FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFCBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FF6C54 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFCCBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #A798FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFCBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFCCBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFCCBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FF6C54 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFCCBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #A798FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFCBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFCCBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFCCBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FF634A 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFCBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #A99BFF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFCABB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFCCBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #BBCCFF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FF634A 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFCBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #A99BFF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFCBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFCCBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #BBCCFF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FF6148 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFCBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #A89AFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBFBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFCCBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #BBC4FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FF0000 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFCBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #8D72FF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFC5BB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFCCBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #BBC9FF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FF422A 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFCBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #9B86FF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFC5BB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFCCBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #BBC9FF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FF422A 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFCBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #9B86FF 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFCBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFCCBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFCCBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FF6349 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFCBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #A798FF 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFCCBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #BBC9FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #BBC8FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FF644B 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #BBCAFF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #9982FF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #BBCCFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #BBC3FF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #BBBFFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FF674E 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #BBC4FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #7556FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #BBCCFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #BBC3FF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #BBBFFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FF674E 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #BBC4FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #7556FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #BBCCFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #BBC3FF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #BBBFFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FF674E 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #BBC4FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #7556FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #BBCCFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #BBC3FF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #BBBEFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FF684E 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #BBC4FF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #795AFF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #BBCCFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #BBC3FF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #BBBEFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FF664C 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #BBC4FF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #7759FF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFCCBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #BBC3FF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #BBBEFF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FF1608 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #BBC4FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #472BFF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFC0BB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #BBC3FF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #B0A6FF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FF1608 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #BBC4FF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #472BFF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFC6BB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #BBC3FF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #BBBAFF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FF482F 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #BBC4FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #6344FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFC6BB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #BBC3FF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #BBBAFF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FF482F 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #BBC1FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #0000FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$`North America`$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC4BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBC2FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF0000 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBC8FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #9F8CFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #B9B7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFA89F 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #B4ADFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #5134FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #BBC7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBC2FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #B9B7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFA9A2 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #AB9FFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #0000FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$`North America`$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #D2D2D2 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF8A77 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #D2D2D2 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFB0AA 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #D2D2D2 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF8672 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #EFD9C4 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #D2D2D2 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #EFD9C4 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC2FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #DAD4CE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #D2D2D2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #D2D2D2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FF8A77 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #EFD9C3 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBC6FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFB0AA 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #D2D2D2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF0000 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #EFD9C3 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBC2FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$`North America`$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBDDFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFA69C 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBDDFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FF9180 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBDDFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF0000 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #D2D2D2 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FF9180 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBDDFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #D2D2D2 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFA69C 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBEBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBDDFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF0000 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #BBDDFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBCAFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FF9180 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #BBDDFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FF0000 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #BBDDFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #BBCAFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FF9180 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #BBDDFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFA69C 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #BBDDFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #BBDDFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FF6F56 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #BBDDFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FF9180 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #BBDDFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #BBDDFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #BBCAFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FF9180 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #BBDDFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #BBDDFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #BBCAFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC6BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFC9BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFCBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFC1BB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFC8BB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFCBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBEBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFC9BB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC2FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC1BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFC9BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC3FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC1BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFC9BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC3FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBCBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFC9BB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBBFFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC1BB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFCBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBBCFF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC8BB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFCBBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC7FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC8BB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFCBBB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC7FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC8BB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCBFF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC0FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBCBFF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC6FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC0FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFC9BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC7FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC2BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFC9BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBBBFF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC3BB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFC9BB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBBBFF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFC4BB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFC9BB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBBCFF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC4BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFC9BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBBCFF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFC6BB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFC9BB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC3FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFC6BB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFC9BB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC3FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC0BB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFC9BB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #9F8CFF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC0BB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFC9BB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #9F8CFF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC0BB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFC9BB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #9F8CFF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC0BB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFC9BB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #9F8CFF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC0BB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFC9BB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #9F8CFF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFBDBB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFC9BB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #6F4FFF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFBFBB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFC9BB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #7455FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFC2BB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFC9BB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #A595FF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFBDBB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFC9BB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #6A4BFF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFBABA 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFC9BB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #4D30FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFC0BB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFC9BB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #9077FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFCBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFCBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #FFCBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC6BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFC9BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #FFCBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC1BB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #FFC8BB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #FFCBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFBEBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #FFC9BB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC2FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC1BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #FFC9BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC3FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC1BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #FFC9BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC3FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFBCBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #FFC9BB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBBFFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC1BB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFCBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBBCFF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC8BB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFCBBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC7FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC8BB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFCBBB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC7FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC8BB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBCBFF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC0FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #BBCBFF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBC6FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC0FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC3BB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFCBBB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BAB8FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFBDBB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFC6BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBBBFF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFBEBB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFC6BB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBBBFF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFBFBB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFC6BB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
150 #BBBCFF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFBFBB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFC6BB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
153 #BBBCFF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFC1BB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC8BB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
156 #A99CFF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFBCBB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC6BB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
159 #A99CFF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFBBBB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC6BB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
162 #6748FF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFBBBB 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC7BB 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
165 #6646FF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFBBBB 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC7BB 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
168 #6646FF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFBBBB 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC7BB 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
171 #6646FF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFBBBB 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC6BB 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
174 #7E60FF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FFA399 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC5BB 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
177 #7151FF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FFB0AB 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC6BB 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
180 #5536FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FFBDBB 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC6BB 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
183 #947CFF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF9586 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC4BB 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
186 #452AFF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF988A 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC4BB 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
189 #0702FF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FFA79E 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC2BB 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
192 #7F61FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
197 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
198 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFCBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFCBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
201 #FFCBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC6BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFC9BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
204 #FFCBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFC1BB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
206 #FFC8BB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
207 #FFCBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFBEBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
209 #FFC9BB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
210 #BBC2FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFC1BB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
212 #FFC9BB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
213 #BBC3FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFC1BB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
215 #FFC9BB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
216 #BBC3FF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFBCBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
218 #FFC9BB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
219 #BBBFFF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFC1BB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFCBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
222 #BBBCFF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC8BB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFCBBB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
225 #BBC7FF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC8BB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFCBBB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
228 #BBC7FF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC8BB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
230 #BBCBFF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
231 #BBC0FF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
232 #BBCBFF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
233 #BBC6FF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
234 #BBC0FF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFC3BB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
236 #FFCBBB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
237 #BAB8FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
238 #FFB2AE 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
239 #FFC7BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
240 #6242FF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
241 #FFB2AE 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC3BB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
243 #BBBBFF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
244 #FFB7B5 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
245 #FFC3BB 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
246 #BBBCFF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
247 #FFB8B6 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
248 #FFC4BB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
249 #BBBBFF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
250 #FFBDBB 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
251 #FFC7BB 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
252 #A696FF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
253 #FFBCBB 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
254 #FFC6BB 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
255 #A899FF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
256 #FFAAA3 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
257 #FFC5BB 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
258 #6141FF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
259 #FFAAA3 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
260 #FFC5BB 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
261 #6243FF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
262 #FFA79F 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
263 #FFC3BB 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
264 #6647FF 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
265 #FF8A78 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
266 #FFC0BB 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
267 #6647FF 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
268 #FF6248 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
269 #FFBCBB 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
270 #7E60FF 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
271 #FF321C 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
272 #FFBABA 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
273 #7050FF 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
274 #FF492F 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
275 #FFBCBB 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
276 #5336FF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
277 #FF6147 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
278 #FFBCBB 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
279 #937BFF 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
280 #FF0000 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
281 #FFB8B6 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
282 #4429FF 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
283 #FF1005 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
284 #FFB6B3 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
285 #0000FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
286 #FF3D25 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
287 #FFB0AB 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
288 #7E61FF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$`North America`$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC5BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCBFF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC1FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FF9D91 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCBFF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #A99BFF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FF8976 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCBFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #9E8BFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FF8470 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCBFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #9C87FF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FF826E 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCBFF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #9B85FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC3BB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCBFF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC2FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC3BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCBFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC2FF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCBFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC2FF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBCBFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC2FF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBCBFF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC2FF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC1BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBCBFF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC0FF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FF9688 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBCBFF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
51 #A594FF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FF6046 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBCBFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
54 #876BFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FF563C 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBCBFF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
57 #8265FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FF4D33 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBCBFF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
60 #7D5FFF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
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350 #BBC3FF 21 4 1 104 20.5 21.5 3.5 4.5 NA 0.1 1
351 #9B86FF 21 4 1 104 20.5 21.5 3.5 4.5 NA 0.1 1
352 #BBC7FF 22 4 1 109 21.5 22.5 3.5 4.5 NA 0.1 1
353 #BBC3FF 22 4 1 109 21.5 22.5 3.5 4.5 NA 0.1 1
354 #BBC1FF 22 4 1 109 21.5 22.5 3.5 4.5 NA 0.1 1
355 #BBC7FF 23 4 1 114 22.5 23.5 3.5 4.5 NA 0.1 1
356 #BBC3FF 23 4 1 114 22.5 23.5 3.5 4.5 NA 0.1 1
357 #BBC1FF 23 4 1 114 22.5 23.5 3.5 4.5 NA 0.1 1
358 #BBC7FF 24 4 1 119 23.5 24.5 3.5 4.5 NA 0.1 1
359 #BBC3FF 24 4 1 119 23.5 24.5 3.5 4.5 NA 0.1 1
360 #BBC1FF 24 4 1 119 23.5 24.5 3.5 4.5 NA 0.1 1
361 #BBC7FF 25 4 1 124 24.5 25.5 3.5 4.5 NA 0.1 1
362 #BBC3FF 25 4 1 124 24.5 25.5 3.5 4.5 NA 0.1 1
363 #BBC1FF 25 4 1 124 24.5 25.5 3.5 4.5 NA 0.1 1
364 #BBC7FF 26 4 1 129 25.5 26.5 3.5 4.5 NA 0.1 1
365 #BBC3FF 26 4 1 129 25.5 26.5 3.5 4.5 NA 0.1 1
366 #BBC1FF 26 4 1 129 25.5 26.5 3.5 4.5 NA 0.1 1
367 #BBCBFF 27 4 1 134 26.5 27.5 3.5 4.5 NA 0.1 1
368 #BBC3FF 27 4 1 134 26.5 27.5 3.5 4.5 NA 0.1 1
369 #B9B8FF 27 4 1 134 26.5 27.5 3.5 4.5 NA 0.1 1
370 #FFC7BB 28 4 1 139 27.5 28.5 3.5 4.5 NA 0.1 1
371 #BBC3FF 28 4 1 139 27.5 28.5 3.5 4.5 NA 0.1 1
372 #9C88FF 28 4 1 139 27.5 28.5 3.5 4.5 NA 0.1 1
373 #FFBCBB 29 4 1 144 28.5 29.5 3.5 4.5 NA 0.1 1
374 #BBC3FF 29 4 1 144 28.5 29.5 3.5 4.5 NA 0.1 1
375 #8265FF 29 4 1 144 28.5 29.5 3.5 4.5 NA 0.1 1
376 #FFBCBB 30 4 1 149 29.5 30.5 3.5 4.5 NA 0.1 1
377 #BBC3FF 30 4 1 149 29.5 30.5 3.5 4.5 NA 0.1 1
378 #7F62FF 30 4 1 149 29.5 30.5 3.5 4.5 NA 0.1 1
379 #FFB9B8 31 4 1 154 30.5 31.5 3.5 4.5 NA 0.1 1
380 #BBC2FF 31 4 1 154 30.5 31.5 3.5 4.5 NA 0.1 1
381 #795BFF 31 4 1 154 30.5 31.5 3.5 4.5 NA 0.1 1
382 #FFB9B8 32 4 1 159 31.5 32.5 3.5 4.5 NA 0.1 1
383 #BBC2FF 32 4 1 159 31.5 32.5 3.5 4.5 NA 0.1 1
384 #795BFF 32 4 1 159 31.5 32.5 3.5 4.5 NA 0.1 1
385 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
386 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
387 #BBC0FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
388 #FFC0BB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
389 #BBCBFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
390 #BBC0FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
391 #FFC0BB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
392 #BBCBFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
393 #BBC0FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
394 #FFC0BB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
395 #BBCBFF 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
396 #BBC0FF 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
397 #FFC0BB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
398 #BBCBFF 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
399 #BBC0FF 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
400 #FFAAA2 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
401 #BBCBFF 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
402 #B0A6FF 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
403 #FF6046 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
404 #BBCBFF 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
405 #866AFF 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
406 #FF472E 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
407 #BBCAFF 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
408 #7859FF 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
409 #FF442B 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
410 #BBCAFF 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
411 #7455FF 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
412 #FF492F 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
413 #BBCAFF 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
414 #7354FF 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
415 #FFBFBB 11 5 1 55 10.5 11.5 4.5 5.5 NA 0.1 1
416 #BBCAFF 11 5 1 55 10.5 11.5 4.5 5.5 NA 0.1 1
417 #AFA6FF 11 5 1 55 10.5 11.5 4.5 5.5 NA 0.1 1
418 #FFC1BB 12 5 1 60 11.5 12.5 4.5 5.5 NA 0.1 1
419 #BBC9FF 12 5 1 60 11.5 12.5 4.5 5.5 NA 0.1 1
420 #AFA6FF 12 5 1 60 11.5 12.5 4.5 5.5 NA 0.1 1
421 #FFC4BB 13 5 1 65 12.5 13.5 4.5 5.5 NA 0.1 1
422 #BBC8FF 13 5 1 65 12.5 13.5 4.5 5.5 NA 0.1 1
423 #AFA5FF 13 5 1 65 12.5 13.5 4.5 5.5 NA 0.1 1
424 #FFC7BB 14 5 1 70 13.5 14.5 4.5 5.5 NA 0.1 1
425 #BBC6FF 14 5 1 70 13.5 14.5 4.5 5.5 NA 0.1 1
426 #AFA5FF 14 5 1 70 13.5 14.5 4.5 5.5 NA 0.1 1
427 #BBCCFF 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
428 #BBC4FF 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
429 #AFA5FF 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
430 #BBCBFF 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
431 #BBC3FF 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
432 #A799FF 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
433 #FFC2BB 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
434 #BBC1FF 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
435 #795AFF 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
436 #FFA9A2 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
437 #BBBFFF 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
438 #462BFF 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
439 #FFA69D 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
440 #BBBEFF 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
441 #3A21FF 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
442 #FFA195 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
443 #BBBDFF 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
444 #2B17FF 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
445 #FFA196 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
446 #BBBDFF 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
447 #2A16FF 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
448 #BBC5FF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
449 #BBBCFF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
450 #ADA2FF 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
451 #BBC5FF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
452 #BBBBFF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
453 #ADA1FF 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
454 #BBC5FF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
455 #BBBBFF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
456 #ACA1FF 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
457 #BBC5FF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
458 #BAB9FF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
459 #AA9DFF 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
460 #BBC5FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
461 #B9B7FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
462 #A798FF 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
463 #BBCAFF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
464 #B8B6FF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
465 #937AFF 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
466 #FFC1BB 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
467 #B6B2FF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
468 #6647FF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
469 #FFABA4 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
470 #B6B1FF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
471 #2F19FF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
472 #FFABA3 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
473 #B5B0FF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
474 #2613FF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
475 #FFA49B 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
476 #B5AFFF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
477 #0000FF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
478 #FFA49B 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
479 #B5AFFF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
480 #0000FF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
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16 NA NA NA
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18 NA NA NA
19 NA NA NA
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25 NA NA NA
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36 NA NA NA
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324 NA NA NA
325 NA NA NA
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327 NA NA NA
328 NA NA NA
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330 NA NA NA
331 NA NA NA
332 NA NA NA
333 NA NA NA
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335 NA NA NA
336 NA NA NA
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341 NA NA NA
342 NA NA NA
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344 NA NA NA
345 NA NA NA
346 NA NA NA
347 NA NA NA
348 NA NA NA
349 NA NA NA
350 NA NA NA
351 NA NA NA
352 NA NA NA
353 NA NA NA
354 NA NA NA
355 NA NA NA
356 NA NA NA
357 NA NA NA
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359 NA NA NA
360 NA NA NA
361 NA NA NA
362 NA NA NA
363 NA NA NA
364 NA NA NA
365 NA NA NA
366 NA NA NA
367 NA NA NA
368 NA NA NA
369 NA NA NA
370 NA NA NA
371 NA NA NA
372 NA NA NA
373 NA NA NA
374 NA NA NA
375 NA NA NA
376 NA NA NA
377 NA NA NA
378 NA NA NA
379 NA NA NA
380 NA NA NA
381 NA NA NA
382 NA NA NA
383 NA NA NA
384 NA NA NA
385 NA NA NA
386 NA NA NA
387 NA NA NA
388 NA NA NA
389 NA NA NA
390 NA NA NA
391 NA NA NA
392 NA NA NA
393 NA NA NA
394 NA NA NA
395 NA NA NA
396 NA NA NA
397 NA NA NA
398 NA NA NA
399 NA NA NA
400 NA NA NA
401 NA NA NA
402 NA NA NA
403 NA NA NA
404 NA NA NA
405 NA NA NA
406 NA NA NA
407 NA NA NA
408 NA NA NA
409 NA NA NA
410 NA NA NA
411 NA NA NA
412 NA NA NA
413 NA NA NA
414 NA NA NA
415 NA NA NA
416 NA NA NA
417 NA NA NA
418 NA NA NA
419 NA NA NA
420 NA NA NA
421 NA NA NA
422 NA NA NA
423 NA NA NA
424 NA NA NA
425 NA NA NA
426 NA NA NA
427 NA NA NA
428 NA NA NA
429 NA NA NA
430 NA NA NA
431 NA NA NA
432 NA NA NA
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435 NA NA NA
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438 NA NA NA
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440 NA NA NA
441 NA NA NA
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443 NA NA NA
444 NA NA NA
445 NA NA NA
446 NA NA NA
447 NA NA NA
448 NA NA NA
449 NA NA NA
450 NA NA NA
451 NA NA NA
452 NA NA NA
453 NA NA NA
454 NA NA NA
455 NA NA NA
456 NA NA NA
457 NA NA NA
458 NA NA NA
459 NA NA NA
460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
$`North America`$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBCCFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCAFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBCBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCAFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBBEFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFB0AB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCAFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBBCFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFACA6 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCAFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBBCFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFACA6 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCAFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBBCFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBCBFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBCAFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC9FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #BBCCFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBCAFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC7FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
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19 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #FF8976 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #FFBDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #FF745D 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #FFBDBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBC5FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FFB2AE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #FF593F 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #FF8A78 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #FF3A23 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #FF8976 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #FFC7BB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFBDBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #FFC7BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #FF1508 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #FF8B78 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #FFCCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #FF0000 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #FF8976 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #FFCCBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
$`North America`$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFB3AF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC5BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFABA4 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC5BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFA69D 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFC5BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FF9B8E 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #FF7A64 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #FFC0BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #FF7A64 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #FFC0BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FF8875 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC9FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #FF735C 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #FF735C 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #FFBFBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FF8875 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #FFC2BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #FF8B79 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #FFCBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #FF836F 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #FFBABA 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #BBCCFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFA49B 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #FFC2BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #FF2613 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #FF7A64 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #FFC2BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #FF1709 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #FF7760 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #FFC6BB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FF8875 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
47 #FFC2BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
48 #BBC9FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
49 #FF2311 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
50 #FF7A64 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
51 #FFC6BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
52 #FF0B03 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
53 #FF765E 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
54 #FFC6BB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
55 #FF8875 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
56 #FFC2BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
57 #BBC9FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
58 #FF2311 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
59 #FF7A64 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
60 #FFC6BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
61 #FF1206 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
62 #FF755E 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
63 #FFBDBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
64 #FFAAA2 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
65 #FFC2BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
66 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
67 #FF2714 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
68 #FF7A64 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
69 #FFBFBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
70 #FF1608 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
71 #FF755E 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
72 #FFC2BB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
73 #FF8875 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
74 #FFC2BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
75 #BBC9FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
76 #FF2311 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
77 #FF7A64 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
78 #FFC6BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
79 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
80 #FF755E 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
81 #FFC6BB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
82 #FF8875 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
83 #FFC2BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
84 #BBC9FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
85 #FF2311 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
86 #FF7A64 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
87 #FFC6BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
88 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
89 #FF755E 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
90 #FFC6BB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$`North America`$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF5A40 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBDDFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBDDFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCCFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF3C24 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBDDFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FF7760 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBDDFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF3720 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBDDFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FF0000 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #BBDDFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FFC2BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #BBDDFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FF0000 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #BBDDFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF644A 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBDDFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FF0000 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #BBDDFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBCAFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FF0000 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #BBDDFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FFC2BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #BBDDFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #FF0000 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #BBDDFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FF644A 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #BBDDFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #BBCBFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FF0000 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #BBDDFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBCAFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FF0000 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #FF5A40 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #BBDDFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #FF7A63 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #BBDDFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #BBCBFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #FF3C24 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #BBDDFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #BBCAFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FF3720 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #BBDDFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FFC2BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #BBDDFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBCCFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #FF0000 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #BBDDFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #BBCAFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #FF644A 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #BBDDFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #BBCBFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #FF0000 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #BBDDFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #BBCAFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FF0000 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #BBDDFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FFC2BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #BBDDFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBCCFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #FF0000 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #BBDDFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #BBCAFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #FF644A 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #BBDDFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #BBCBFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #FF0000 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #BBDDFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #BBCAFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FF0000 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #BBDDFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FFC2BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #BBDDFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBCCFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #FF0000 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #BBDDFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #FF8F7E 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #BBDDFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #BBCBFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #FF5A40 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #BBDDFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #BBCAFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #FF3C24 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #BBDDFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FFC6BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #BBDDFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBCCFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #FF3720 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #BBDDFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #BBCAFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #FF644A 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #BBDDFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #BBCBFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #FF0000 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #BBDDFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #BBCAFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FF0000 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #BBDDFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FFC2BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #BBDDFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBCCFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #BBDDFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #BBCAFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #FF644A 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #BBDDFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #BBCBFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #FF0000 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #BBDDFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #BBCAFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FF0000 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #BBDDFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FFC2BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #BBDDFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBCCFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #BBDDFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #BBCAFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #FF644A 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #BBDDFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #BBCBFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #FF0000 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #BBDDFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #BBCAFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$`North America`$eff
$`North America`$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 2.637704e-14
2 2 1 2 NA lightgray 0.5 1 NA 2.637704e-14
3 3 1 3 NA lightgray 0.5 1 NA 2.637704e-14
4 4 1 4 NA lightgray 0.5 1 NA 2.637704e-14
5 5 1 5 NA lightgray 0.5 1 NA 2.637704e-14
6 6 1 6 NA lightgray 0.5 1 NA 2.637704e-14
7 7 1 7 NA lightgray 0.5 1 NA 2.637704e-14
xmax ymin ymax
1 2.637802e-14 -Inf Inf
2 2.637802e-14 -Inf Inf
3 2.637802e-14 -Inf Inf
4 2.637802e-14 -Inf Inf
5 2.637802e-14 -Inf Inf
6 2.637802e-14 -Inf Inf
7 2.637802e-14 -Inf Inf
$`North America`$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 2.637774e-14 2.637774e-14 1 1 1 0.875 1.125 black 0.5
2 2.637774e-14 2.637774e-14 2 1 2 1.875 2.125 black 0.5
3 3.526728e-10 6.601906e-10 3 1 3 2.875 3.125 black 0.5
4 -9.118766e-10 3.461658e-10 4 1 4 3.875 4.125 black 0.5
5 -4.132864e-08 1.076012e-07 5 1 5 4.875 5.125 black 0.5
6 -2.322912e-01 6.023659e-02 6 1 6 5.875 6.125 black 0.5
7 -2.586368e+00 -1.112267e-01 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$`North America`$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 2.637753e-14 2.637753e-14 0.7 1.3 1 1 1 NA white 0.5
2 2.637753e-14 2.637753e-14 1.7 2.3 2 1 2 NA white 0.5
3 -1.916743e-10 1.917271e-10 2.7 3.3 3 1 3 NA white 0.5
4 -3.505835e-10 3.506362e-10 3.7 4.3 4 1 4 NA white 0.5
5 -1.701125e-08 1.701130e-08 4.7 5.3 5 1 5 NA white 0.5
6 -6.394821e-02 6.394821e-02 5.7 6.3 6 1 6 NA white 0.5
7 -7.410985e-01 7.410985e-01 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$`North America`$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 2.637753e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 2.637753e-14 NALED 8.1% 3 1 3 black 3 0 0.5 -1 NA
4 2.637753e-14 NALED 41.7% 4 1 4 black 3 0 0.5 -1 NA
5 2.637753e-14 NALED 20.4% 5 1 5 black 3 0 0.5 -1 NA
6 2.637753e-14 NALED 29.5% 6 1 6 black 3 0 0.5 -1 NA
7 2.637753e-14 NALED 43.0% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 2.637753e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -1.916743e-10 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -3.505835e-10 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.701125e-08 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -6.394821e-02 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 -7.410985e-01 ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 2.637753e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 1.917271e-10 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 3.506362e-10 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.701130e-08 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 6.394821e-02 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 7.410985e-01 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 2.637753e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 2.637753e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 2.637753e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 2.637753e-14 ALED 0.00 5 1 5 black 3 0 0.5 2 NA
6 2.637753e-14 ALED 0.13 6 1 6 black 3 0 0.5 2 NA
7 2.637753e-14 ALED 1.48 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 7 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$.all_cats
$.all_cats$d1
$.all_cats$d1$mpg
$.all_cats$d1$mpg$overlay
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
11 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
12 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
13 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
14 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
15 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
16 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
17 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
18 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
19 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
20 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
21 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
22 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
23 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
24 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
25 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
26 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
27 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
28 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
29 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
30 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 2.637802e-14
2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
4 -Inf Inf 1.197341e-14 2.637802e-14
5 -Inf Inf 1.197341e-14 2.637802e-14
6 -Inf Inf 1.197341e-14 2.637802e-14
7 -Inf Inf 1.197341e-14 2.637802e-14
8 -Inf Inf 1.197341e-14 2.637802e-14
9 -Inf Inf 1.197341e-14 2.637802e-14
10 -Inf Inf 1.197341e-14 2.637802e-14
11 -Inf Inf 1.197341e-14 2.637802e-14
12 -Inf Inf 1.197341e-14 2.637802e-14
13 -Inf Inf 1.197341e-14 2.637802e-14
14 -Inf Inf 1.197341e-14 2.637802e-14
15 -Inf Inf 1.197341e-14 2.637802e-14
16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$mpg$facet
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
11 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
12 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
13 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
14 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
15 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
16 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
17 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
18 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
19 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
20 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
21 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
22 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
23 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
24 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
25 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
26 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
27 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
28 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
29 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
30 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 2.637802e-14
2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
4 -Inf Inf 1.197341e-14 2.637802e-14
5 -Inf Inf 1.197341e-14 2.637802e-14
6 -Inf Inf 1.197341e-14 2.637802e-14
7 -Inf Inf 1.197341e-14 2.637802e-14
8 -Inf Inf 1.197341e-14 2.637802e-14
9 -Inf Inf 1.197341e-14 2.637802e-14
10 -Inf Inf 1.197341e-14 2.637802e-14
11 -Inf Inf 1.197341e-14 2.637802e-14
12 -Inf Inf 1.197341e-14 2.637802e-14
13 -Inf Inf 1.197341e-14 2.637802e-14
14 -Inf Inf 1.197341e-14 2.637802e-14
15 -Inf Inf 1.197341e-14 2.637802e-14
16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$vs
$.all_cats$d1$vs$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
3 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
4 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
5 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
6 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$vs$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
3 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
4 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
5 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
6 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$am
$.all_cats$d1$am$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 6.188871e-03 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.548244e-02 1 2 NA lightgray 0.5 1 NA -Inf
3 1 -6.188870e-03 1 1 NA lightgray 0.5 1 NA -Inf
4 2 -1.548244e-02 1 2 NA lightgray 0.5 1 NA -Inf
5 1 -5.571233e-10 1 1 NA lightgray 0.5 1 NA -Inf
6 2 1.565281e-10 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$am$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 6.188871e-03 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.548244e-02 1 2 NA lightgray 0.5 1 NA -Inf
3 1 -6.188870e-03 1 1 NA lightgray 0.5 1 NA -Inf
4 2 -1.548244e-02 1 2 NA lightgray 0.5 1 NA -Inf
5 1 -5.571233e-10 1 1 NA lightgray 0.5 1 NA -Inf
6 2 1.565281e-10 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$model
$.all_cats$d1$model$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.02256553 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.09317291 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.23524785 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.23524785 1 4 NA lightgray 0.5 1 NA -Inf
5 5 0.09751143 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.26417810 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.26417810 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.26611036 1 8 NA lightgray 0.5 1 NA -Inf
9 9 0.43273667 1 9 NA lightgray 0.5 1 NA -Inf
10 10 0.44616466 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.44616466 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.44616466 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.44616466 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.77949799 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.22030910 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.05364243 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -0.05370066 1 17 NA lightgray 0.5 1 NA -Inf
18 18 0.11220276 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.11220281 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.14577267 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -0.02754194 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -0.36770131 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -0.03661493 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -0.69864333 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -0.69864332 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -0.69864353 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.02590338 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -0.52590572 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.02589030 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -0.85922071 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -0.52591213 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.02590860 1 32 NA lightgray 0.5 1 NA -Inf
33 1 0.21712565 1 1 NA lightgray 0.5 1 NA -Inf
34 2 0.26805387 1 2 NA lightgray 0.5 1 NA -Inf
35 3 0.93059625 1 3 NA lightgray 0.5 1 NA -Inf
36 4 0.76392958 1 4 NA lightgray 0.5 1 NA -Inf
37 5 -0.06882970 1 5 NA lightgray 0.5 1 NA -Inf
38 6 0.09783696 1 6 NA lightgray 0.5 1 NA -Inf
39 7 0.09783696 1 7 NA lightgray 0.5 1 NA -Inf
40 8 0.59590470 1 8 NA lightgray 0.5 1 NA -Inf
41 9 -0.23738828 1 9 NA lightgray 0.5 1 NA -Inf
42 10 -0.08414960 1 10 NA lightgray 0.5 1 NA -Inf
43 11 0.24918374 1 11 NA lightgray 0.5 1 NA -Inf
44 12 0.41585053 1 12 NA lightgray 0.5 1 NA -Inf
45 13 0.41585053 1 13 NA lightgray 0.5 1 NA -Inf
46 14 0.25025654 1 14 NA lightgray 0.5 1 NA -Inf
47 15 0.75025654 1 15 NA lightgray 0.5 1 NA -Inf
48 16 0.44517303 1 16 NA lightgray 0.5 1 NA -Inf
49 17 1.32301938 1 17 NA lightgray 0.5 1 NA -Inf
50 18 1.15711598 1 18 NA lightgray 0.5 1 NA -Inf
51 19 0.99044927 1 19 NA lightgray 0.5 1 NA -Inf
52 20 0.95687941 1 20 NA lightgray 0.5 1 NA -Inf
53 21 1.13019402 1 21 NA lightgray 0.5 1 NA -Inf
54 22 1.63702005 1 22 NA lightgray 0.5 1 NA -Inf
55 23 1.30593413 1 23 NA lightgray 0.5 1 NA -Inf
56 24 1.97260071 1 24 NA lightgray 0.5 1 NA -Inf
57 25 1.97260070 1 25 NA lightgray 0.5 1 NA -Inf
58 26 2.30593426 1 26 NA lightgray 0.5 1 NA -Inf
59 27 2.63319411 1 27 NA lightgray 0.5 1 NA -Inf
60 28 2.07246592 1 28 NA lightgray 0.5 1 NA -Inf
61 29 2.57245050 1 29 NA lightgray 0.5 1 NA -Inf
62 30 2.23178490 1 30 NA lightgray 0.5 1 NA -Inf
63 31 1.89843292 1 31 NA lightgray 0.5 1 NA -Inf
64 32 2.56509702 1 32 NA lightgray 0.5 1 NA -Inf
65 1 -0.19456011 1 1 NA lightgray 0.5 1 NA -Inf
66 2 -0.36122678 1 2 NA lightgray 0.5 1 NA -Inf
67 3 -0.69534840 1 3 NA lightgray 0.5 1 NA -Inf
68 4 -0.52868173 1 4 NA lightgray 0.5 1 NA -Inf
69 5 -0.02868173 1 5 NA lightgray 0.5 1 NA -Inf
70 6 -0.36201506 1 6 NA lightgray 0.5 1 NA -Inf
71 7 -0.36201506 1 7 NA lightgray 0.5 1 NA -Inf
72 8 -0.86201506 1 8 NA lightgray 0.5 1 NA -Inf
73 9 -0.19534839 1 9 NA lightgray 0.5 1 NA -Inf
74 10 -0.36201506 1 10 NA lightgray 0.5 1 NA -Inf
75 11 -0.69534839 1 11 NA lightgray 0.5 1 NA -Inf
76 12 -0.86201518 1 12 NA lightgray 0.5 1 NA -Inf
77 13 -0.86201518 1 13 NA lightgray 0.5 1 NA -Inf
78 14 -1.02975453 1 14 NA lightgray 0.5 1 NA -Inf
79 15 -0.52994744 1 15 NA lightgray 0.5 1 NA -Inf
80 16 -0.39153060 1 16 NA lightgray 0.5 1 NA -Inf
81 17 -1.26931872 1 17 NA lightgray 0.5 1 NA -Inf
82 18 -1.26931875 1 18 NA lightgray 0.5 1 NA -Inf
83 19 -1.10265208 1 19 NA lightgray 0.5 1 NA -Inf
84 20 -1.10265208 1 20 NA lightgray 0.5 1 NA -Inf
85 21 -1.10265208 1 21 NA lightgray 0.5 1 NA -Inf
86 22 -1.26931874 1 22 NA lightgray 0.5 1 NA -Inf
87 23 -1.26931920 1 23 NA lightgray 0.5 1 NA -Inf
88 24 -1.27395738 1 24 NA lightgray 0.5 1 NA -Inf
89 25 -1.27395738 1 25 NA lightgray 0.5 1 NA -Inf
90 26 -1.60729073 1 26 NA lightgray 0.5 1 NA -Inf
91 27 -1.60729073 1 27 NA lightgray 0.5 1 NA -Inf
92 28 -1.54656020 1 28 NA lightgray 0.5 1 NA -Inf
93 29 -1.54656020 1 29 NA lightgray 0.5 1 NA -Inf
94 30 -1.37256419 1 30 NA lightgray 0.5 1 NA -Inf
95 31 -1.37252079 1 31 NA lightgray 0.5 1 NA -Inf
96 32 -1.53918841 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
10 Inf 1.197341e-14 2.637802e-14
11 Inf 1.197341e-14 2.637802e-14
12 Inf 1.197341e-14 2.637802e-14
13 Inf 1.197341e-14 2.637802e-14
14 Inf 1.197341e-14 2.637802e-14
15 Inf 1.197341e-14 2.637802e-14
16 Inf 1.197341e-14 2.637802e-14
17 Inf 1.197341e-14 2.637802e-14
18 Inf 1.197341e-14 2.637802e-14
19 Inf 1.197341e-14 2.637802e-14
20 Inf 1.197341e-14 2.637802e-14
21 Inf 1.197341e-14 2.637802e-14
22 Inf 1.197341e-14 2.637802e-14
23 Inf 1.197341e-14 2.637802e-14
24 Inf 1.197341e-14 2.637802e-14
25 Inf 1.197341e-14 2.637802e-14
26 Inf 1.197341e-14 2.637802e-14
27 Inf 1.197341e-14 2.637802e-14
28 Inf 1.197341e-14 2.637802e-14
29 Inf 1.197341e-14 2.637802e-14
30 Inf 1.197341e-14 2.637802e-14
31 Inf 1.197341e-14 2.637802e-14
32 Inf 1.197341e-14 2.637802e-14
33 Inf 1.197341e-14 2.637802e-14
34 Inf 1.197341e-14 2.637802e-14
35 Inf 1.197341e-14 2.637802e-14
36 Inf 1.197341e-14 2.637802e-14
37 Inf 1.197341e-14 2.637802e-14
38 Inf 1.197341e-14 2.637802e-14
39 Inf 1.197341e-14 2.637802e-14
40 Inf 1.197341e-14 2.637802e-14
41 Inf 1.197341e-14 2.637802e-14
42 Inf 1.197341e-14 2.637802e-14
43 Inf 1.197341e-14 2.637802e-14
44 Inf 1.197341e-14 2.637802e-14
45 Inf 1.197341e-14 2.637802e-14
46 Inf 1.197341e-14 2.637802e-14
47 Inf 1.197341e-14 2.637802e-14
48 Inf 1.197341e-14 2.637802e-14
49 Inf 1.197341e-14 2.637802e-14
50 Inf 1.197341e-14 2.637802e-14
51 Inf 1.197341e-14 2.637802e-14
52 Inf 1.197341e-14 2.637802e-14
53 Inf 1.197341e-14 2.637802e-14
54 Inf 1.197341e-14 2.637802e-14
55 Inf 1.197341e-14 2.637802e-14
56 Inf 1.197341e-14 2.637802e-14
57 Inf 1.197341e-14 2.637802e-14
58 Inf 1.197341e-14 2.637802e-14
59 Inf 1.197341e-14 2.637802e-14
60 Inf 1.197341e-14 2.637802e-14
61 Inf 1.197341e-14 2.637802e-14
62 Inf 1.197341e-14 2.637802e-14
63 Inf 1.197341e-14 2.637802e-14
64 Inf 1.197341e-14 2.637802e-14
65 Inf 1.197341e-14 2.637802e-14
66 Inf 1.197341e-14 2.637802e-14
67 Inf 1.197341e-14 2.637802e-14
68 Inf 1.197341e-14 2.637802e-14
69 Inf 1.197341e-14 2.637802e-14
70 Inf 1.197341e-14 2.637802e-14
71 Inf 1.197341e-14 2.637802e-14
72 Inf 1.197341e-14 2.637802e-14
73 Inf 1.197341e-14 2.637802e-14
74 Inf 1.197341e-14 2.637802e-14
75 Inf 1.197341e-14 2.637802e-14
76 Inf 1.197341e-14 2.637802e-14
77 Inf 1.197341e-14 2.637802e-14
78 Inf 1.197341e-14 2.637802e-14
79 Inf 1.197341e-14 2.637802e-14
80 Inf 1.197341e-14 2.637802e-14
81 Inf 1.197341e-14 2.637802e-14
82 Inf 1.197341e-14 2.637802e-14
83 Inf 1.197341e-14 2.637802e-14
84 Inf 1.197341e-14 2.637802e-14
85 Inf 1.197341e-14 2.637802e-14
86 Inf 1.197341e-14 2.637802e-14
87 Inf 1.197341e-14 2.637802e-14
88 Inf 1.197341e-14 2.637802e-14
89 Inf 1.197341e-14 2.637802e-14
90 Inf 1.197341e-14 2.637802e-14
91 Inf 1.197341e-14 2.637802e-14
92 Inf 1.197341e-14 2.637802e-14
93 Inf 1.197341e-14 2.637802e-14
94 Inf 1.197341e-14 2.637802e-14
95 Inf 1.197341e-14 2.637802e-14
96 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$model$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.02256553 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.09317291 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.23524785 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.23524785 1 4 NA lightgray 0.5 1 NA -Inf
5 5 0.09751143 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.26417810 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.26417810 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.26611036 1 8 NA lightgray 0.5 1 NA -Inf
9 9 0.43273667 1 9 NA lightgray 0.5 1 NA -Inf
10 10 0.44616466 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.44616466 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.44616466 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.44616466 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.77949799 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.22030910 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.05364243 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -0.05370066 1 17 NA lightgray 0.5 1 NA -Inf
18 18 0.11220276 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.11220281 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.14577267 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -0.02754194 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -0.36770131 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -0.03661493 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -0.69864333 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -0.69864332 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -0.69864353 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.02590338 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -0.52590572 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.02589030 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -0.85922071 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -0.52591213 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.02590860 1 32 NA lightgray 0.5 1 NA -Inf
33 1 0.21712565 1 1 NA lightgray 0.5 1 NA -Inf
34 2 0.26805387 1 2 NA lightgray 0.5 1 NA -Inf
35 3 0.93059625 1 3 NA lightgray 0.5 1 NA -Inf
36 4 0.76392958 1 4 NA lightgray 0.5 1 NA -Inf
37 5 -0.06882970 1 5 NA lightgray 0.5 1 NA -Inf
38 6 0.09783696 1 6 NA lightgray 0.5 1 NA -Inf
39 7 0.09783696 1 7 NA lightgray 0.5 1 NA -Inf
40 8 0.59590470 1 8 NA lightgray 0.5 1 NA -Inf
41 9 -0.23738828 1 9 NA lightgray 0.5 1 NA -Inf
42 10 -0.08414960 1 10 NA lightgray 0.5 1 NA -Inf
43 11 0.24918374 1 11 NA lightgray 0.5 1 NA -Inf
44 12 0.41585053 1 12 NA lightgray 0.5 1 NA -Inf
45 13 0.41585053 1 13 NA lightgray 0.5 1 NA -Inf
46 14 0.25025654 1 14 NA lightgray 0.5 1 NA -Inf
47 15 0.75025654 1 15 NA lightgray 0.5 1 NA -Inf
48 16 0.44517303 1 16 NA lightgray 0.5 1 NA -Inf
49 17 1.32301938 1 17 NA lightgray 0.5 1 NA -Inf
50 18 1.15711598 1 18 NA lightgray 0.5 1 NA -Inf
51 19 0.99044927 1 19 NA lightgray 0.5 1 NA -Inf
52 20 0.95687941 1 20 NA lightgray 0.5 1 NA -Inf
53 21 1.13019402 1 21 NA lightgray 0.5 1 NA -Inf
54 22 1.63702005 1 22 NA lightgray 0.5 1 NA -Inf
55 23 1.30593413 1 23 NA lightgray 0.5 1 NA -Inf
56 24 1.97260071 1 24 NA lightgray 0.5 1 NA -Inf
57 25 1.97260070 1 25 NA lightgray 0.5 1 NA -Inf
58 26 2.30593426 1 26 NA lightgray 0.5 1 NA -Inf
59 27 2.63319411 1 27 NA lightgray 0.5 1 NA -Inf
60 28 2.07246592 1 28 NA lightgray 0.5 1 NA -Inf
61 29 2.57245050 1 29 NA lightgray 0.5 1 NA -Inf
62 30 2.23178490 1 30 NA lightgray 0.5 1 NA -Inf
63 31 1.89843292 1 31 NA lightgray 0.5 1 NA -Inf
64 32 2.56509702 1 32 NA lightgray 0.5 1 NA -Inf
65 1 -0.19456011 1 1 NA lightgray 0.5 1 NA -Inf
66 2 -0.36122678 1 2 NA lightgray 0.5 1 NA -Inf
67 3 -0.69534840 1 3 NA lightgray 0.5 1 NA -Inf
68 4 -0.52868173 1 4 NA lightgray 0.5 1 NA -Inf
69 5 -0.02868173 1 5 NA lightgray 0.5 1 NA -Inf
70 6 -0.36201506 1 6 NA lightgray 0.5 1 NA -Inf
71 7 -0.36201506 1 7 NA lightgray 0.5 1 NA -Inf
72 8 -0.86201506 1 8 NA lightgray 0.5 1 NA -Inf
73 9 -0.19534839 1 9 NA lightgray 0.5 1 NA -Inf
74 10 -0.36201506 1 10 NA lightgray 0.5 1 NA -Inf
75 11 -0.69534839 1 11 NA lightgray 0.5 1 NA -Inf
76 12 -0.86201518 1 12 NA lightgray 0.5 1 NA -Inf
77 13 -0.86201518 1 13 NA lightgray 0.5 1 NA -Inf
78 14 -1.02975453 1 14 NA lightgray 0.5 1 NA -Inf
79 15 -0.52994744 1 15 NA lightgray 0.5 1 NA -Inf
80 16 -0.39153060 1 16 NA lightgray 0.5 1 NA -Inf
81 17 -1.26931872 1 17 NA lightgray 0.5 1 NA -Inf
82 18 -1.26931875 1 18 NA lightgray 0.5 1 NA -Inf
83 19 -1.10265208 1 19 NA lightgray 0.5 1 NA -Inf
84 20 -1.10265208 1 20 NA lightgray 0.5 1 NA -Inf
85 21 -1.10265208 1 21 NA lightgray 0.5 1 NA -Inf
86 22 -1.26931874 1 22 NA lightgray 0.5 1 NA -Inf
87 23 -1.26931920 1 23 NA lightgray 0.5 1 NA -Inf
88 24 -1.27395738 1 24 NA lightgray 0.5 1 NA -Inf
89 25 -1.27395738 1 25 NA lightgray 0.5 1 NA -Inf
90 26 -1.60729073 1 26 NA lightgray 0.5 1 NA -Inf
91 27 -1.60729073 1 27 NA lightgray 0.5 1 NA -Inf
92 28 -1.54656020 1 28 NA lightgray 0.5 1 NA -Inf
93 29 -1.54656020 1 29 NA lightgray 0.5 1 NA -Inf
94 30 -1.37256419 1 30 NA lightgray 0.5 1 NA -Inf
95 31 -1.37252079 1 31 NA lightgray 0.5 1 NA -Inf
96 32 -1.53918841 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
10 Inf 1.197341e-14 2.637802e-14
11 Inf 1.197341e-14 2.637802e-14
12 Inf 1.197341e-14 2.637802e-14
13 Inf 1.197341e-14 2.637802e-14
14 Inf 1.197341e-14 2.637802e-14
15 Inf 1.197341e-14 2.637802e-14
16 Inf 1.197341e-14 2.637802e-14
17 Inf 1.197341e-14 2.637802e-14
18 Inf 1.197341e-14 2.637802e-14
19 Inf 1.197341e-14 2.637802e-14
20 Inf 1.197341e-14 2.637802e-14
21 Inf 1.197341e-14 2.637802e-14
22 Inf 1.197341e-14 2.637802e-14
23 Inf 1.197341e-14 2.637802e-14
24 Inf 1.197341e-14 2.637802e-14
25 Inf 1.197341e-14 2.637802e-14
26 Inf 1.197341e-14 2.637802e-14
27 Inf 1.197341e-14 2.637802e-14
28 Inf 1.197341e-14 2.637802e-14
29 Inf 1.197341e-14 2.637802e-14
30 Inf 1.197341e-14 2.637802e-14
31 Inf 1.197341e-14 2.637802e-14
32 Inf 1.197341e-14 2.637802e-14
33 Inf 1.197341e-14 2.637802e-14
34 Inf 1.197341e-14 2.637802e-14
35 Inf 1.197341e-14 2.637802e-14
36 Inf 1.197341e-14 2.637802e-14
37 Inf 1.197341e-14 2.637802e-14
38 Inf 1.197341e-14 2.637802e-14
39 Inf 1.197341e-14 2.637802e-14
40 Inf 1.197341e-14 2.637802e-14
41 Inf 1.197341e-14 2.637802e-14
42 Inf 1.197341e-14 2.637802e-14
43 Inf 1.197341e-14 2.637802e-14
44 Inf 1.197341e-14 2.637802e-14
45 Inf 1.197341e-14 2.637802e-14
46 Inf 1.197341e-14 2.637802e-14
47 Inf 1.197341e-14 2.637802e-14
48 Inf 1.197341e-14 2.637802e-14
49 Inf 1.197341e-14 2.637802e-14
50 Inf 1.197341e-14 2.637802e-14
51 Inf 1.197341e-14 2.637802e-14
52 Inf 1.197341e-14 2.637802e-14
53 Inf 1.197341e-14 2.637802e-14
54 Inf 1.197341e-14 2.637802e-14
55 Inf 1.197341e-14 2.637802e-14
56 Inf 1.197341e-14 2.637802e-14
57 Inf 1.197341e-14 2.637802e-14
58 Inf 1.197341e-14 2.637802e-14
59 Inf 1.197341e-14 2.637802e-14
60 Inf 1.197341e-14 2.637802e-14
61 Inf 1.197341e-14 2.637802e-14
62 Inf 1.197341e-14 2.637802e-14
63 Inf 1.197341e-14 2.637802e-14
64 Inf 1.197341e-14 2.637802e-14
65 Inf 1.197341e-14 2.637802e-14
66 Inf 1.197341e-14 2.637802e-14
67 Inf 1.197341e-14 2.637802e-14
68 Inf 1.197341e-14 2.637802e-14
69 Inf 1.197341e-14 2.637802e-14
70 Inf 1.197341e-14 2.637802e-14
71 Inf 1.197341e-14 2.637802e-14
72 Inf 1.197341e-14 2.637802e-14
73 Inf 1.197341e-14 2.637802e-14
74 Inf 1.197341e-14 2.637802e-14
75 Inf 1.197341e-14 2.637802e-14
76 Inf 1.197341e-14 2.637802e-14
77 Inf 1.197341e-14 2.637802e-14
78 Inf 1.197341e-14 2.637802e-14
79 Inf 1.197341e-14 2.637802e-14
80 Inf 1.197341e-14 2.637802e-14
81 Inf 1.197341e-14 2.637802e-14
82 Inf 1.197341e-14 2.637802e-14
83 Inf 1.197341e-14 2.637802e-14
84 Inf 1.197341e-14 2.637802e-14
85 Inf 1.197341e-14 2.637802e-14
86 Inf 1.197341e-14 2.637802e-14
87 Inf 1.197341e-14 2.637802e-14
88 Inf 1.197341e-14 2.637802e-14
89 Inf 1.197341e-14 2.637802e-14
90 Inf 1.197341e-14 2.637802e-14
91 Inf 1.197341e-14 2.637802e-14
92 Inf 1.197341e-14 2.637802e-14
93 Inf 1.197341e-14 2.637802e-14
94 Inf 1.197341e-14 2.637802e-14
95 Inf 1.197341e-14 2.637802e-14
96 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$gear
$.all_cats$d1$gear$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.330118650 1 3 NA lightgray 0.5 1 NA -Inf
4 1 -0.238915351 1 1 NA lightgray 0.5 1 NA -Inf
5 2 0.247194038 1 2 NA lightgray 0.5 1 NA -Inf
6 3 0.379947472 1 3 NA lightgray 0.5 1 NA -Inf
7 1 0.120477486 1 1 NA lightgray 0.5 1 NA -Inf
8 2 -0.249169983 1 2 NA lightgray 0.5 1 NA -Inf
9 3 -0.049828823 1 3 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$gear$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.330118650 1 3 NA lightgray 0.5 1 NA -Inf
4 1 -0.238915351 1 1 NA lightgray 0.5 1 NA -Inf
5 2 0.247194038 1 2 NA lightgray 0.5 1 NA -Inf
6 3 0.379947472 1 3 NA lightgray 0.5 1 NA -Inf
7 1 0.120477486 1 1 NA lightgray 0.5 1 NA -Inf
8 2 -0.249169983 1 2 NA lightgray 0.5 1 NA -Inf
9 3 -0.049828823 1 3 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$carb
$.all_cats$d1$carb$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 5.489234e-03 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -1.898356e-03 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -1.898365e-03 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 -1.898365e-03 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 -1.898365e-03 1 -1 NA lightgray 0.5 1 NA -Inf
6 1 -5.489341e-03 1 -1 NA lightgray 0.5 1 NA -Inf
7 2 1.898392e-03 1 -1 NA lightgray 0.5 1 NA -Inf
8 3 1.898401e-03 1 -1 NA lightgray 0.5 1 NA -Inf
9 4 1.898401e-03 1 -1 NA lightgray 0.5 1 NA -Inf
10 8 1.898406e-03 1 -1 NA lightgray 0.5 1 NA -Inf
11 1 1.076012e-07 1 -1 NA lightgray 0.5 1 NA -Inf
12 2 -3.593977e-08 1 -1 NA lightgray 0.5 1 NA -Inf
13 3 -3.594863e-08 1 -1 NA lightgray 0.5 1 NA -Inf
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xmin xmax ymin ymax
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2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
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14 -Inf Inf 1.197341e-14 2.637802e-14
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16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d2
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63 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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67 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
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99 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
100 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
101 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
102 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
103 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
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105 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
106 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
107 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
108 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
109 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
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139 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
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147 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
148 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
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150 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
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alpha width height
1 NA NA NA
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171 NA NA NA
172 NA NA NA
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174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
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180 NA NA NA
$.all_cats$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
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15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
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33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
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299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
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305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
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311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
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444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
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507 #FFBBBB 9 17 1 273 8.5 9.5 16.5 17.5 NA 0.1 1
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1166 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
1167 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
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1169 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
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1187 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
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1189 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
1190 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
1191 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
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1193 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
1194 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
1195 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
1196 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
1197 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
1198 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
1199 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
1200 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
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1209 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
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1218 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
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1221 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
1222 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
1223 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
1224 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
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1227 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
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1251 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
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1256 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
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1277 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
1278 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
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1282 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
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1284 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
1285 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
1286 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
1287 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
1288 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
1289 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
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1305 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
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1311 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
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1375 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
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1714 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
1715 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
1716 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
1717 #FFBBBB 3 26 1 90 2.5 3.5 25.5 26.5 NA 0.1 1
1718 #FFBBBB 3 26 1 90 2.5 3.5 25.5 26.5 NA 0.1 1
1719 #FFBBBB 3 26 1 90 2.5 3.5 25.5 26.5 NA 0.1 1
1720 #FFBBBB 4 26 1 122 3.5 4.5 25.5 26.5 NA 0.1 1
1721 #FFBBBB 4 26 1 122 3.5 4.5 25.5 26.5 NA 0.1 1
1722 #FFBBBB 4 26 1 122 3.5 4.5 25.5 26.5 NA 0.1 1
1723 #FFBBBB 5 26 1 154 4.5 5.5 25.5 26.5 NA 0.1 1
1724 #FFBBBB 5 26 1 154 4.5 5.5 25.5 26.5 NA 0.1 1
1725 #FFBBBB 5 26 1 154 4.5 5.5 25.5 26.5 NA 0.1 1
1726 #FFBBBB 6 26 1 186 5.5 6.5 25.5 26.5 NA 0.1 1
1727 #FFBBBB 6 26 1 186 5.5 6.5 25.5 26.5 NA 0.1 1
1728 #FFBBBB 6 26 1 186 5.5 6.5 25.5 26.5 NA 0.1 1
1729 #FFBBBB 7 26 1 218 6.5 7.5 25.5 26.5 NA 0.1 1
1730 #FFBBBB 7 26 1 218 6.5 7.5 25.5 26.5 NA 0.1 1
1731 #FFBBBB 7 26 1 218 6.5 7.5 25.5 26.5 NA 0.1 1
1732 #FFBBBB 8 26 1 250 7.5 8.5 25.5 26.5 NA 0.1 1
1733 #FFBBBB 8 26 1 250 7.5 8.5 25.5 26.5 NA 0.1 1
1734 #FFBBBB 8 26 1 250 7.5 8.5 25.5 26.5 NA 0.1 1
1735 #FFBBBB 9 26 1 282 8.5 9.5 25.5 26.5 NA 0.1 1
1736 #FFBBBB 9 26 1 282 8.5 9.5 25.5 26.5 NA 0.1 1
1737 #FFBBBB 9 26 1 282 8.5 9.5 25.5 26.5 NA 0.1 1
1738 #FFBBBB 10 26 1 314 9.5 10.5 25.5 26.5 NA 0.1 1
1739 #FFBBBB 10 26 1 314 9.5 10.5 25.5 26.5 NA 0.1 1
1740 #FFBBBB 10 26 1 314 9.5 10.5 25.5 26.5 NA 0.1 1
1741 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
1742 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
1743 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
1744 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
1745 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
1746 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
1747 #FFBBBB 3 27 1 91 2.5 3.5 26.5 27.5 NA 0.1 1
1748 #FFBBBB 3 27 1 91 2.5 3.5 26.5 27.5 NA 0.1 1
1749 #FFBBBB 3 27 1 91 2.5 3.5 26.5 27.5 NA 0.1 1
1750 #FFBBBB 4 27 1 123 3.5 4.5 26.5 27.5 NA 0.1 1
1751 #FFBBBB 4 27 1 123 3.5 4.5 26.5 27.5 NA 0.1 1
1752 #FFBBBB 4 27 1 123 3.5 4.5 26.5 27.5 NA 0.1 1
1753 #FFBBBB 5 27 1 155 4.5 5.5 26.5 27.5 NA 0.1 1
1754 #FFBBBB 5 27 1 155 4.5 5.5 26.5 27.5 NA 0.1 1
1755 #FFBBBB 5 27 1 155 4.5 5.5 26.5 27.5 NA 0.1 1
1756 #FFBBBB 6 27 1 187 5.5 6.5 26.5 27.5 NA 0.1 1
1757 #FFBBBB 6 27 1 187 5.5 6.5 26.5 27.5 NA 0.1 1
1758 #FFBBBB 6 27 1 187 5.5 6.5 26.5 27.5 NA 0.1 1
1759 #FFBBBB 7 27 1 219 6.5 7.5 26.5 27.5 NA 0.1 1
1760 #FFBBBB 7 27 1 219 6.5 7.5 26.5 27.5 NA 0.1 1
1761 #FFBBBB 7 27 1 219 6.5 7.5 26.5 27.5 NA 0.1 1
1762 #FFBBBB 8 27 1 251 7.5 8.5 26.5 27.5 NA 0.1 1
1763 #FFBBBB 8 27 1 251 7.5 8.5 26.5 27.5 NA 0.1 1
1764 #FFBBBB 8 27 1 251 7.5 8.5 26.5 27.5 NA 0.1 1
1765 #FFBBBB 9 27 1 283 8.5 9.5 26.5 27.5 NA 0.1 1
1766 #FFBBBB 9 27 1 283 8.5 9.5 26.5 27.5 NA 0.1 1
1767 #FFBBBB 9 27 1 283 8.5 9.5 26.5 27.5 NA 0.1 1
1768 #FFBBBB 10 27 1 315 9.5 10.5 26.5 27.5 NA 0.1 1
1769 #FFBBBB 10 27 1 315 9.5 10.5 26.5 27.5 NA 0.1 1
1770 #FFBBBB 10 27 1 315 9.5 10.5 26.5 27.5 NA 0.1 1
1771 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
1772 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
1773 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
1774 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
1775 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
1776 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
1777 #FFBBBB 3 28 1 92 2.5 3.5 27.5 28.5 NA 0.1 1
1778 #FFBBBB 3 28 1 92 2.5 3.5 27.5 28.5 NA 0.1 1
1779 #FFBBBB 3 28 1 92 2.5 3.5 27.5 28.5 NA 0.1 1
1780 #FFBBBB 4 28 1 124 3.5 4.5 27.5 28.5 NA 0.1 1
1781 #FFBBBB 4 28 1 124 3.5 4.5 27.5 28.5 NA 0.1 1
1782 #FFBBBB 4 28 1 124 3.5 4.5 27.5 28.5 NA 0.1 1
1783 #FFBBBB 5 28 1 156 4.5 5.5 27.5 28.5 NA 0.1 1
1784 #FFBBBB 5 28 1 156 4.5 5.5 27.5 28.5 NA 0.1 1
1785 #FFBBBB 5 28 1 156 4.5 5.5 27.5 28.5 NA 0.1 1
1786 #FFBBBB 6 28 1 188 5.5 6.5 27.5 28.5 NA 0.1 1
1787 #FFBBBB 6 28 1 188 5.5 6.5 27.5 28.5 NA 0.1 1
1788 #FFBBBB 6 28 1 188 5.5 6.5 27.5 28.5 NA 0.1 1
1789 #FFBBBB 7 28 1 220 6.5 7.5 27.5 28.5 NA 0.1 1
1790 #FFBBBB 7 28 1 220 6.5 7.5 27.5 28.5 NA 0.1 1
1791 #FFBBBB 7 28 1 220 6.5 7.5 27.5 28.5 NA 0.1 1
1792 #FFBBBB 8 28 1 252 7.5 8.5 27.5 28.5 NA 0.1 1
1793 #FFBBBB 8 28 1 252 7.5 8.5 27.5 28.5 NA 0.1 1
1794 #FFBBBB 8 28 1 252 7.5 8.5 27.5 28.5 NA 0.1 1
1795 #FFBBBB 9 28 1 284 8.5 9.5 27.5 28.5 NA 0.1 1
1796 #FFBBBB 9 28 1 284 8.5 9.5 27.5 28.5 NA 0.1 1
1797 #FFBBBB 9 28 1 284 8.5 9.5 27.5 28.5 NA 0.1 1
1798 #FFBBBB 10 28 1 316 9.5 10.5 27.5 28.5 NA 0.1 1
1799 #FFBBBB 10 28 1 316 9.5 10.5 27.5 28.5 NA 0.1 1
1800 #FFBBBB 10 28 1 316 9.5 10.5 27.5 28.5 NA 0.1 1
1801 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
1802 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
1803 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
1804 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
1805 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
1806 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
1807 #FFBBBB 3 29 1 93 2.5 3.5 28.5 29.5 NA 0.1 1
1808 #FFBBBB 3 29 1 93 2.5 3.5 28.5 29.5 NA 0.1 1
1809 #FFBBBB 3 29 1 93 2.5 3.5 28.5 29.5 NA 0.1 1
1810 #FFBBBB 4 29 1 125 3.5 4.5 28.5 29.5 NA 0.1 1
1811 #FFBBBB 4 29 1 125 3.5 4.5 28.5 29.5 NA 0.1 1
1812 #FFBBBB 4 29 1 125 3.5 4.5 28.5 29.5 NA 0.1 1
1813 #FFBBBB 5 29 1 157 4.5 5.5 28.5 29.5 NA 0.1 1
1814 #FFBBBB 5 29 1 157 4.5 5.5 28.5 29.5 NA 0.1 1
1815 #FFBBBB 5 29 1 157 4.5 5.5 28.5 29.5 NA 0.1 1
1816 #FFBBBB 6 29 1 189 5.5 6.5 28.5 29.5 NA 0.1 1
1817 #FFBBBB 6 29 1 189 5.5 6.5 28.5 29.5 NA 0.1 1
1818 #FFBBBB 6 29 1 189 5.5 6.5 28.5 29.5 NA 0.1 1
1819 #FFBBBB 7 29 1 221 6.5 7.5 28.5 29.5 NA 0.1 1
1820 #FFBBBB 7 29 1 221 6.5 7.5 28.5 29.5 NA 0.1 1
1821 #FFBBBB 7 29 1 221 6.5 7.5 28.5 29.5 NA 0.1 1
1822 #FFBBBB 8 29 1 253 7.5 8.5 28.5 29.5 NA 0.1 1
1823 #FFBBBB 8 29 1 253 7.5 8.5 28.5 29.5 NA 0.1 1
1824 #FFBBBB 8 29 1 253 7.5 8.5 28.5 29.5 NA 0.1 1
1825 #FFBBBB 9 29 1 285 8.5 9.5 28.5 29.5 NA 0.1 1
1826 #FFBBBB 9 29 1 285 8.5 9.5 28.5 29.5 NA 0.1 1
1827 #FFBBBB 9 29 1 285 8.5 9.5 28.5 29.5 NA 0.1 1
1828 #FFBBBB 10 29 1 317 9.5 10.5 28.5 29.5 NA 0.1 1
1829 #FFBBBB 10 29 1 317 9.5 10.5 28.5 29.5 NA 0.1 1
1830 #FFBBBB 10 29 1 317 9.5 10.5 28.5 29.5 NA 0.1 1
1831 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
1832 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
1833 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
1834 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
1835 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
1836 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
1837 #FFBBBB 3 30 1 94 2.5 3.5 29.5 30.5 NA 0.1 1
1838 #FFBBBB 3 30 1 94 2.5 3.5 29.5 30.5 NA 0.1 1
1839 #FFBBBB 3 30 1 94 2.5 3.5 29.5 30.5 NA 0.1 1
1840 #FFBBBB 4 30 1 126 3.5 4.5 29.5 30.5 NA 0.1 1
1841 #FFBBBB 4 30 1 126 3.5 4.5 29.5 30.5 NA 0.1 1
1842 #FFBBBB 4 30 1 126 3.5 4.5 29.5 30.5 NA 0.1 1
1843 #FFBBBB 5 30 1 158 4.5 5.5 29.5 30.5 NA 0.1 1
1844 #FFBBBB 5 30 1 158 4.5 5.5 29.5 30.5 NA 0.1 1
1845 #FFBBBB 5 30 1 158 4.5 5.5 29.5 30.5 NA 0.1 1
1846 #FFBBBB 6 30 1 190 5.5 6.5 29.5 30.5 NA 0.1 1
1847 #FFBBBB 6 30 1 190 5.5 6.5 29.5 30.5 NA 0.1 1
1848 #FFBBBB 6 30 1 190 5.5 6.5 29.5 30.5 NA 0.1 1
1849 #FFBBBB 7 30 1 222 6.5 7.5 29.5 30.5 NA 0.1 1
1850 #FFBBBB 7 30 1 222 6.5 7.5 29.5 30.5 NA 0.1 1
1851 #FFBBBB 7 30 1 222 6.5 7.5 29.5 30.5 NA 0.1 1
1852 #FFBBBB 8 30 1 254 7.5 8.5 29.5 30.5 NA 0.1 1
1853 #FFBBBB 8 30 1 254 7.5 8.5 29.5 30.5 NA 0.1 1
1854 #FFBBBB 8 30 1 254 7.5 8.5 29.5 30.5 NA 0.1 1
1855 #FFBBBB 9 30 1 286 8.5 9.5 29.5 30.5 NA 0.1 1
1856 #FFBBBB 9 30 1 286 8.5 9.5 29.5 30.5 NA 0.1 1
1857 #FFBBBB 9 30 1 286 8.5 9.5 29.5 30.5 NA 0.1 1
1858 #FFBBBB 10 30 1 318 9.5 10.5 29.5 30.5 NA 0.1 1
1859 #FFBBBB 10 30 1 318 9.5 10.5 29.5 30.5 NA 0.1 1
1860 #FFBBBB 10 30 1 318 9.5 10.5 29.5 30.5 NA 0.1 1
1861 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
1862 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
1863 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
1864 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
1865 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
1866 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
1867 #FFBBBB 3 31 1 95 2.5 3.5 30.5 31.5 NA 0.1 1
1868 #FFBBBB 3 31 1 95 2.5 3.5 30.5 31.5 NA 0.1 1
1869 #FFBBBB 3 31 1 95 2.5 3.5 30.5 31.5 NA 0.1 1
1870 #FFBBBB 4 31 1 127 3.5 4.5 30.5 31.5 NA 0.1 1
1871 #FFBBBB 4 31 1 127 3.5 4.5 30.5 31.5 NA 0.1 1
1872 #FFBBBB 4 31 1 127 3.5 4.5 30.5 31.5 NA 0.1 1
1873 #FFBBBB 5 31 1 159 4.5 5.5 30.5 31.5 NA 0.1 1
1874 #FFBBBB 5 31 1 159 4.5 5.5 30.5 31.5 NA 0.1 1
1875 #FFBBBB 5 31 1 159 4.5 5.5 30.5 31.5 NA 0.1 1
1876 #FFBBBB 6 31 1 191 5.5 6.5 30.5 31.5 NA 0.1 1
1877 #FFBBBB 6 31 1 191 5.5 6.5 30.5 31.5 NA 0.1 1
1878 #FFBBBB 6 31 1 191 5.5 6.5 30.5 31.5 NA 0.1 1
1879 #FFBBBB 7 31 1 223 6.5 7.5 30.5 31.5 NA 0.1 1
1880 #FFBBBB 7 31 1 223 6.5 7.5 30.5 31.5 NA 0.1 1
1881 #FFBBBB 7 31 1 223 6.5 7.5 30.5 31.5 NA 0.1 1
1882 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1883 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1884 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1885 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1886 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1887 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1888 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1889 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1890 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1891 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1892 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1893 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1894 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1895 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1896 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1897 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1898 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1899 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1900 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1901 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1902 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1903 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1904 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1905 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1906 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1907 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1908 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1909 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1910 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1911 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1912 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1913 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1914 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1915 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1916 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1917 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1918 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1919 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1920 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1921 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1922 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1923 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1924 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1925 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1926 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1927 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1928 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1929 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1930 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1931 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1932 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1933 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1934 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1935 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1936 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1937 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1938 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1939 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1940 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1941 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1942 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1943 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1944 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1945 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1946 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1947 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1948 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1949 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1950 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1951 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1952 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1953 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1954 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1955 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1956 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1957 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1958 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1959 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1960 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1961 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1962 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1963 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1964 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1965 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1966 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1967 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1968 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1969 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1970 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1971 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1972 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1973 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1974 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1975 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1976 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1977 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1978 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1979 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1980 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1981 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1982 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1983 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1984 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1985 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1986 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1987 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1988 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1989 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1990 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1991 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1992 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1993 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1994 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1995 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1996 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1997 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1998 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1999 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2000 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2001 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2002 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2003 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2004 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2005 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2006 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2007 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2008 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2009 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2010 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2011 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2012 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2013 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2014 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2015 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2016 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2017 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2018 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2019 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2020 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2021 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2022 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2023 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2024 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2025 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2026 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2027 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2028 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2029 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2030 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2031 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2032 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2033 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2034 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2035 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2036 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2037 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2038 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2039 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2040 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2041 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2042 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2043 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2044 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2045 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2046 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2047 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2048 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2049 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2050 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2051 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2052 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2053 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2054 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2055 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2056 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2057 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2058 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2059 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2060 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2061 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2062 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2063 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2064 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2065 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2066 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2067 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2068 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2069 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2070 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2071 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2072 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2073 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2074 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2075 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2076 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2077 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2078 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2079 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2080 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2081 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2082 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2083 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2084 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2085 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2086 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2087 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2088 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2089 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2090 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2091 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2092 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2093 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2094 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2095 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2096 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2097 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2098 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2099 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2100 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2101 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2102 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2103 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2104 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2105 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2106 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2107 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2108 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2109 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2110 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2111 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2112 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2113 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2114 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2115 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2116 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2117 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2118 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2119 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2120 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2121 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2122 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2123 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2124 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2125 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2126 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2127 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2128 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2129 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2130 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2131 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2132 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2133 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2134 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2135 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2136 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2137 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2138 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2139 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2140 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2141 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2142 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2143 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2144 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2145 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2146 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2147 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2148 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2149 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2150 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2151 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2152 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2153 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2154 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2155 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2156 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2157 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2158 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2159 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2160 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2161 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2162 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2163 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2164 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
2165 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
2166 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
2167 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
2168 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
2169 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
2170 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
2171 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
2172 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
2173 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
2174 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
2175 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
2176 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
2177 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
2178 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
2179 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
2180 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
2181 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
2182 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
2183 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
2184 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
2185 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
2186 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
2187 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
2188 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
2189 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
2190 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
2191 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
2192 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
2193 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
2194 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
2195 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
2196 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
2197 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
2198 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
2199 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
2200 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
2201 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
2202 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
2203 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
2204 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
2205 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
2206 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
2207 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
2208 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
2209 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
2210 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
2211 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
2212 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
2213 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
2214 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
2215 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
2216 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
2217 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
2218 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
2219 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
2220 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
2221 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
2222 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
2223 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
2224 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
2225 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
2226 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
2227 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
2228 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
2229 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
2230 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
2231 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
2232 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
2233 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
2234 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
2235 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
2236 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
2237 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
2238 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
2239 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
2240 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
2241 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
2242 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
2243 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
2244 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
2245 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
2246 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
2247 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
2248 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
2249 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
2250 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
2251 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
2252 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
2253 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
2254 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
2255 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
2256 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
2257 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
2258 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
2259 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
2260 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
2261 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
2262 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
2263 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
2264 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
2265 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
2266 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
2267 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
2268 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
2269 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
2270 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
2271 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
2272 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
2273 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
2274 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
2275 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
2276 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
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14 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
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31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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48 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
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50 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
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52 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
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103 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
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144 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
145 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
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155 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
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171 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
172 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
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240 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
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$.all_cats$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
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39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
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54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
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60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
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63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
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67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
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69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
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78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
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84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
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93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
152 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
153 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
154 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
155 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
156 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
157 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
158 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
159 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
160 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
161 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
162 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
163 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
164 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
165 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
166 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
167 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
168 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
169 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
170 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
171 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
172 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
173 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
174 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
175 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
176 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
177 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
178 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
179 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
180 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
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182 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
183 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
184 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
185 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
186 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
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188 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
189 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
190 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
191 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
192 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
193 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
194 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
195 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
196 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
197 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
198 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
199 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
200 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
201 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
202 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
203 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
204 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
205 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
206 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
207 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
208 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
209 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
210 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
211 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
212 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
213 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
214 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
215 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
216 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
217 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
218 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
219 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
220 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
221 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
222 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
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224 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
225 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
226 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
227 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
228 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
229 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
230 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
231 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
232 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
233 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
234 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
235 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
236 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
237 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
238 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
239 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
240 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
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243 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
244 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
245 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
246 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
247 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
248 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
249 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
250 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
251 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
252 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
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256 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
257 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
258 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
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260 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
261 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
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264 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
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275 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
276 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
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279 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
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285 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
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291 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
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294 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
295 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
296 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
297 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
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306 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
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330 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
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395 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
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80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
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82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
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84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
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89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
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96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
302 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
303 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
304 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
305 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
306 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
307 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
308 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
309 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
310 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
311 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
312 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
313 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
314 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
315 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
316 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
317 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
318 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
319 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
320 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
321 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
322 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
323 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
324 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
325 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
326 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
327 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
328 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
329 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
330 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
331 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
332 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
333 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
334 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
335 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
336 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
337 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
338 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
339 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
340 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
341 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
342 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
343 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
344 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
345 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
346 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
347 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
348 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
349 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
350 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
351 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
352 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
353 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
354 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
355 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
356 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
357 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
358 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
359 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
360 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
361 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
362 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
363 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
364 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
365 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
366 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
367 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
368 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
369 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
370 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
371 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
372 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
373 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
374 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
375 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
376 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
377 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
378 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
379 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
380 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
381 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
382 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
383 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
384 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
385 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
386 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
387 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
388 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
389 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
390 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
391 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
392 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
393 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
394 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
395 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
396 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
397 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
398 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
399 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
400 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
401 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
402 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
403 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
404 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
405 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
406 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
407 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
408 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
409 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
410 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
411 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
412 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
413 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
414 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
415 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
416 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
417 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
418 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
419 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
420 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
421 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
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423 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
424 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
425 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
426 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
427 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
428 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
429 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
430 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
431 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
432 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
433 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
434 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
435 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
436 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
437 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
438 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
439 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
440 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
441 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
442 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
443 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
444 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
445 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
446 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
447 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
448 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
449 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
450 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
451 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
452 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
453 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
454 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
455 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
456 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
457 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
458 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
459 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
460 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
461 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
462 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
463 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
464 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
465 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
466 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
467 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
468 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
469 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
470 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
471 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
472 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
473 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
474 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
475 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
476 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
477 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
478 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
479 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
480 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
481 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
482 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
483 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
484 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
485 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
486 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
487 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
488 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
489 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
490 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
491 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
492 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
493 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
494 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
495 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
496 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
497 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
498 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
499 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
500 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
501 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
502 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
503 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
504 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
505 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
506 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
507 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
508 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
509 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
510 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
511 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
512 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
513 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
514 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
515 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
516 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
517 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
518 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
519 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
520 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
521 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
522 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
523 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
524 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
525 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
526 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
527 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
528 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
529 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
530 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
531 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
532 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
533 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
534 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
535 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
536 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
537 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
538 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
539 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
540 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
541 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
542 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
543 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
544 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
545 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
546 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
547 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
548 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
549 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
550 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
551 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
552 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
553 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
554 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
555 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
556 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
557 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
558 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
559 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
560 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
561 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
562 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
563 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
564 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
565 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
566 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
567 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
568 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
569 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
570 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
571 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
572 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
573 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
574 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
575 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
576 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
577 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
578 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
579 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
580 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
581 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
582 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
583 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
584 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
585 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
586 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
587 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
588 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
589 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
590 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
591 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
592 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
593 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
594 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
595 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
596 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
597 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
598 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
599 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
600 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
601 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
602 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
603 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
604 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
605 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
606 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
607 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
608 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
609 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
610 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
611 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
612 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
613 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
614 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
615 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
616 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
617 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
618 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
619 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
620 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
621 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
622 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
623 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
624 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
625 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
626 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
627 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
628 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
629 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
630 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
631 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
632 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
633 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
634 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
635 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
636 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
637 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
638 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
639 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
640 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
641 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
642 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
643 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
644 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
645 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
646 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
647 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
648 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
649 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
650 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
651 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
652 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
653 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
654 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
655 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
656 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
657 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
658 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
659 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
660 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
661 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
662 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
663 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
664 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
665 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
666 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
667 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
668 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
669 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
670 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
671 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
672 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
673 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
674 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
675 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
676 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
677 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
678 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
679 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
680 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
681 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
682 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
683 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
684 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
685 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
686 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
687 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
688 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
689 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
690 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
691 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
692 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
693 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
694 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
695 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
696 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
697 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
698 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
699 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
700 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
701 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
702 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
703 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
704 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
705 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
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707 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
708 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
709 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
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711 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
712 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
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$.all_cats$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
20 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
21 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
22 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
23 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
24 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
25 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
$.all_cats$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
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155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
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161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
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164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
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167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
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171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
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173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
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180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
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185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
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192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
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195 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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197 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
198 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
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200 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
201 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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203 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
204 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
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207 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
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209 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
210 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
211 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
212 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
213 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
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215 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
216 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
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221 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
222 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
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228 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
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234 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
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240 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
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245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
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249 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
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251 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
252 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
253 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
254 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
255 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
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261 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
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264 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
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267 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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270 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
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282 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
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293 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
294 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
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330 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
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$.all_cats$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
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11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
26 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
27 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
28 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
29 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
30 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
31 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
32 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
33 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
34 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
35 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
36 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
37 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
38 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
39 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
40 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
41 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
42 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
43 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
50 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
51 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
52 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
53 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
54 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
$.all_cats$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
32 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
33 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
34 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
35 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
36 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
37 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
44 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
45 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
46 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
47 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
48 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
49 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
50 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
51 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
52 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
53 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
54 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
55 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
56 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
57 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
58 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
59 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
60 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
61 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
62 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
63 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
64 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
65 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
66 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
67 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
68 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
69 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
70 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
71 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
72 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
73 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
77 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
78 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
80 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
81 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
82 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
83 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
84 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
85 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
86 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
87 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
88 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
89 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
90 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$.all_cats$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
62 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
63 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
64 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
65 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
66 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
67 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
68 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
69 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
70 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
71 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
72 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
73 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
80 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
81 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
82 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
83 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
84 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
85 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
86 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
87 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
88 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
89 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
90 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
91 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
92 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
93 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
94 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
95 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
96 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
97 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
98 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
99 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
100 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
101 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
102 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
103 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
104 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
105 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
106 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
107 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
108 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
109 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
110 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
111 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
112 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
113 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
114 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
115 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
116 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
117 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
118 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
119 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
120 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
121 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
122 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
123 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
124 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
125 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
126 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
127 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
128 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
129 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
130 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
131 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
132 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
133 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
134 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
135 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
136 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
137 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
138 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
139 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
140 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
141 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
142 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
143 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
144 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
145 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
158 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
159 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
160 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
161 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
162 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
163 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
164 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
165 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
166 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
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168 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
169 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
170 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
171 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
172 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
173 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
174 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
175 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
176 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
177 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
178 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
179 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
180 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
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100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
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106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
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114 NA NA NA
115 NA NA NA
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120 NA NA NA
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129 NA NA NA
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131 NA NA NA
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151 NA NA NA
152 NA NA NA
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154 NA NA NA
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157 NA NA NA
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160 NA NA NA
161 NA NA NA
162 NA NA NA
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166 NA NA NA
167 NA NA NA
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170 NA NA NA
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$.all_cats$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBDBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBBDFF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBC9FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #BBC5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBDBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBC9FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #BBBDFF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #BBC5FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBDBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBCBFF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #BBC3FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFC6BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #BBC5FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBDBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBCBFF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #BBC3FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFC6BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #BBC5FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBDBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #BBCBFF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #BBC3FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFC5BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBC4FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBDBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #BBCBFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #BBC3FF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFC5BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBC4FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBDBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #BBCBFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #BBC3FF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFC5BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC4FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFAFAA 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #BBCBFF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #BBC0FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFC8BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC4FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFAFAA 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #BBCBFF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #BBC0FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC4FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFB9B8 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #BBCBFF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #BBC2FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC7BB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #BBCBFF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #BBC4FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFB9B8 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #BBCBFF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #BBC2FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFC5BB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #BBCBFF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #BBC5FF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBDBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #BBCBFF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #BBC4FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBFBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFCBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #BBC5FF 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBDBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFCBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #BBC4FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBFBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFCBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #BBC5FF 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBDBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFCBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #BBC4FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBFBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFCBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #BBC5FF 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBDBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFCBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #BBC4FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBFBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFCBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #BBC5FF 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBDBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFCABB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #BBC4FF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBFBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFCBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #BBC4FF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBDBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFCABB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #BBC3FF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBFBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFCBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #BBC4FF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBCBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFCABB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #BBBFFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFC4BB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFCBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #BBC4FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFACA6 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFC8BB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #BBBFFF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFC4BB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFCBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #BBC4FF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFB7B5 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFC7BB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #BBC4FF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFC1BB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFCBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #BBC4FF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBEBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFC6BB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #BBC7FF 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFC7BB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #BBC9FF 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFAAA3 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFC2BB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFCABB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FF9F93 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFC3BB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #BBCBFF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FF7E68 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFC0BB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #BBC7FF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FF8875 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBFBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #BBCBFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FF5238 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBDBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #BBC1FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FF8774 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBDBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #BBCCFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FF452C 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFB8B7 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #BBBEFF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FF8774 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBDBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #BBCCFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FF432A 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFB1AC 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #BBBEFF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FF8774 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBDBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #BBCCFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FF0000 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFA398 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #BBC3FF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FF8571 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBDBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #BBCCFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FF3B23 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFA9A1 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #BBC3FF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FF8672 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBDBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #BBCCFF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FF3C24 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFA9A1 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #BBC3FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FF9B8E 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBDBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFC7BB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FF5A40 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFA9A1 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #BBC8FF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FF9080 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBDBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFCABB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FF4C32 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFA9A1 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #BBC6FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FF9080 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBDBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFCABB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FF2412 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFA297 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #BBC6FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
193 #FFB9B8 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
194 #FFCABB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
195 #BBBFFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
196 #FFC8BB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
197 #FFCABB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
198 #FFCBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
199 #FFB8B7 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
200 #FFC9BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
201 #BBC0FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
202 #FFC4BB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
203 #FFC9BB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
204 #FFCBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
205 #FFB8B7 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
206 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
207 #B5B0FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
208 #FFC4BB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
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3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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50 #BBC2FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
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width height
1 NA NA
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$.all_cats$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFC2BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
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18 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
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20 #BBC2FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #B2ABFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #B8B5FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #AB9EFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #9781FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBC2FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBBCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #BAB9FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #AB9EFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #8A6FFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
32 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
33 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
34 #FFCABB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
35 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
36 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
37 #FFB8B7 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
38 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
39 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
40 #FFCCBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
41 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
42 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
43 #FFC1BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
44 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
45 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
46 #FFC6BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
47 #BBCCFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
48 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
49 #FFB4B1 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
50 #FFBCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
51 #FFC2BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
52 #FF452C 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
53 #FF6A51 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
54 #FF8B79 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
55 #FFB4B2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
56 #FFBCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
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58 #FF0000 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
59 #FF6A51 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
60 #FF8D7B 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
61 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
62 #EDD9C5 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
63 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
64 #FFC2BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
65 #EDD9C5 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
66 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
67 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
68 #F3DAC2 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
69 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
70 #FFC2BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
71 #FFDDBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
72 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
73 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
74 #F3DAC2 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
75 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
76 #FFBCBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
77 #FFDDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
78 #BBC2FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
79 #FCDCBC 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
80 #F3DAC2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
81 #EDD9C4 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
82 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
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84 #BBC6FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
85 #FFC6BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
86 #F3DAC2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
87 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
88 #FFBCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
89 #FFDDBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
90 #BBC2FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$.all_cats$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFC4BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
12 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFC4BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
18 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFCABB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBC9FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
24 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
25 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
29 #BBC7FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
30 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFCBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
35 #BBC7FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
36 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFCBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
41 #BBC6FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
42 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
43 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
47 #BBC6FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
48 #BBC5FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFCBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
53 #BBC6FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
54 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFCBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
59 #BBC6FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
60 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
62 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
63 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
64 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
65 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
66 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
67 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
68 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
69 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
70 #FFBFBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
71 #FFC7BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
72 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
73 #FFB3B0 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
74 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
75 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
76 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
77 #FFC7BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
78 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
79 #FFB1AD 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
80 #FFC0BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
81 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
82 #FFA196 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
83 #FFAEA8 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
84 #FFBDBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
85 #FF8A78 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
86 #FFB6B4 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
87 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
88 #FF5F45 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
89 #FF674E 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
90 #FF6C53 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
91 #FF8976 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
92 #FFB6B4 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
93 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
94 #FF3A22 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
95 #FF573D 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
96 #FF6950 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
97 #FF8976 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
98 #FFB6B4 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
99 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
100 #FF301A 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
101 #FF4F35 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
102 #FF684F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
103 #FF8A78 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
104 #FFB6B4 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
105 #FFC0BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
106 #FF331D 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
107 #FF4F35 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
108 #FF684F 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
109 #FF8976 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
110 #FFB6B4 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
111 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
112 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
113 #FF4F35 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
114 #FF6950 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
115 #FF8976 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
116 #FFB6B4 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
117 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
118 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
119 #FF4F35 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
120 #FF6950 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
121 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
122 #C8D7E8 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
123 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
124 #FFC6BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
125 #C8D7E8 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
126 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
127 #FFC4BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
128 #C8D7E8 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
129 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
130 #FFBEBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
131 #E9D7C7 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
132 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
133 #FFC4BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
134 #C8D7E8 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
135 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
136 #FFBEBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
137 #E9D7C7 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
138 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
139 #C8D7E8 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
140 #C8D7E9 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
141 #C8D7E9 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
142 #FFC6BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
143 #C8D7E9 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
144 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
145 #FFC8BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
146 #C8D7E9 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
147 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
148 #FFBEBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
149 #C8D7E9 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
150 #BBCAFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
151 #FFC4BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #C8D7E9 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBEBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #C8D7E9 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #BBCAFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFC4BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
158 #C8D7E9 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
159 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
160 #FFC6BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
161 #C8D7E9 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
162 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
163 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
164 #C8D7E9 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
165 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
166 #FFC2BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
167 #C8D7E9 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
168 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
169 #FFC4BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
170 #C8D7E9 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
171 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
172 #FFBEBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
173 #C8D7E9 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
174 #BBCAFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
175 #FFC4BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
176 #C8D7E9 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
177 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
178 #FFBEBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
179 #C8D7E9 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
180 #BBCAFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
$.all_cats$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC9FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC5BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCABB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC9FF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFCBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC9FF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFCBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCAFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC6FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCBFF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC6FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC6BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCBFF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC4FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC8BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCBFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC5FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC8BB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCBFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC5FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC2BB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFCCBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC4FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC2BB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFCCBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC4FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC6BB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFCCBB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC8FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFCABB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC8FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFCABB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC8FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFCABB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC8FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFCABB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC8FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFBDBB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFCABB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC6FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFBFBB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFCABB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC7FF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC2BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFCABB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC8FF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFBDBB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFCABB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC6FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFBCBB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFCABB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC6FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFBFBB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFCABB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC6FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFBFBB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFCABB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC7FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFBFBB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFCABB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC7FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFBFBB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFCABB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC7FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFBFBB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFCABB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC7FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFBFBB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFCABB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC7FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFB5B2 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFCABB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC4FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFBBBB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFCABB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC4FF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFBBBB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFCABB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC4FF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFB0AB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFCABB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC3FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFB0AB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFCABB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC3FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCCFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC9FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC5BB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFCABB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC9FF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFCBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC9FF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFCBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBCAFF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC6FF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBCBFF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC6BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBCBFF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC4FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC8BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBCBFF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC5FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC8BB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBCBFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC5FF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC2BB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFCCBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC4FF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC2BB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFCCBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC4FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC6BB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFCCBB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC8FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC3BB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFCABB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC8FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC3BB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFCABB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC8FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC2BB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFCABB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC8FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC2BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFCABB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC8FF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFBDBB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFCABB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBC6FF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFBFBB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFCABB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
150 #BBC7FF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC2BB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFCABB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
153 #BBC7FF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFBDBB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC9BB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
156 #BBC9FF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFBCBB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC8BB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
159 #BBC9FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFBBBB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC6BB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBCCFF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFB7B5 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC8BB 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBC4FF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFAAA3 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC4BB 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBC9FF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFAAA3 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC4BB 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
171 #BBC9FF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFA59B 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC3BB 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
174 #BBCAFF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FF988B 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC1BB 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
177 #BBCAFF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FF8E7D 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC4BB 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
180 #BBC3FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FF9485 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC1BB 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
183 #BBC9FF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF9586 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC2BB 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
186 #BBC9FF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF816C 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC3BB 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
189 #BBC3FF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FF654C 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC1BB 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
192 #BBC2FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCCFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBC9FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC5BB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFCABB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBC9FF 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC8BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFCBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBC9FF 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFCBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBCAFF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
207 #BBC6FF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFC8BB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBCBFF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
210 #BBC6FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFC6BB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBCBFF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
213 #BBC4FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFC8BB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBCBFF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
216 #BBC5FF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFC8BB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBCBFF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
219 #BBC5FF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFC2BB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFCCBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
222 #BBC4FF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC2BB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFCCBB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
225 #BBC4FF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC6BB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFCCBB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
228 #BBC8FF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC3BB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
230 #FFCABB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
231 #BBC8FF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
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237 #BBC8FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
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239 #FFC8BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
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241 #FFBDBB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC8BB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
243 #BBC6FF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
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246 #BBC7FF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
247 #FFBEBB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
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249 #BBC7FF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
250 #FFADA7 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
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252 #BBC9FF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
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254 #FFC4BB 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
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1412 NA NA NA
1413 NA NA NA
1414 NA NA NA
1415 NA NA NA
1416 NA NA NA
1417 NA NA NA
1418 NA NA NA
1419 NA NA NA
1420 NA NA NA
1421 NA NA NA
1422 NA NA NA
1423 NA NA NA
1424 NA NA NA
1425 NA NA NA
1426 NA NA NA
1427 NA NA NA
1428 NA NA NA
1429 NA NA NA
1430 NA NA NA
1431 NA NA NA
1432 NA NA NA
1433 NA NA NA
1434 NA NA NA
1435 NA NA NA
1436 NA NA NA
1437 NA NA NA
1438 NA NA NA
1439 NA NA NA
1440 NA NA NA
$.all_cats$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #BBC8FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC7FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC5FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFCCBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC7FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC1FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFCCBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC7FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC1FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFCCBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC7FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC1FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFCCBB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC7FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC1FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #BBC8FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBC7FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC5FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #BBC9FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBC7FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC4FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFCCBB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBC7FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC1FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFCCBB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBC7FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC1FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFCCBB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBC7FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC1FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFCCBB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBC7FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC1FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
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69 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
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71 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
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75 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
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78 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #BBC8FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBC7FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC5FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #BBCBFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBC7FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC2FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFCCBB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBC7FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC1FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFCCBB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBC7FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC0FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFCCBB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBC7FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC0FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #BBC8FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC7FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC5FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFCCBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC7FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC0FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFCCBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC7FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC0FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFCCBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBC7FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC0FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFCCBB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBC7FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC0FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFCCBB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBC7FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC0FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFCCBB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBC7FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC0FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFCCBB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBC7FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC0FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFCCBB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBC7FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC0FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFCCBB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBC7FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC0FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFCBBB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBC7FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBBEFF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFCABB 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBC7FF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
150 #BBBDFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC8BB 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBC7FF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
153 #B8B6FF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
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156 #B8B5FF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
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158 #BBC7FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
159 #B7B4FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFC8BB 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
161 #BBC7FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
162 #B7B4FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFCBBB 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
164 #BBC7FF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBBFFF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFCBBB 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
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168 #BBBFFF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFCBBB 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
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187 #FFC8BB 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
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1501 #FF8A77 21 6 1 206 20.5 21.5 5.5 6.5 NA 0.1 1
1502 #FFBBBB 21 6 1 206 20.5 21.5 5.5 6.5 NA 0.1 1
1503 #BBCCFF 21 6 1 206 20.5 21.5 5.5 6.5 NA 0.1 1
1504 #FF8B79 22 6 1 216 21.5 22.5 5.5 6.5 NA 0.1 1
1505 #FFBAB9 22 6 1 216 21.5 22.5 5.5 6.5 NA 0.1 1
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1509 #FFC6BB 23 6 1 226 22.5 23.5 5.5 6.5 NA 0.1 1
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1512 #FFC6BB 24 6 1 236 23.5 24.5 5.5 6.5 NA 0.1 1
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1514 #FFB7B5 25 6 1 246 24.5 25.5 5.5 6.5 NA 0.1 1
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1521 #FFC6BB 27 6 1 266 26.5 27.5 5.5 6.5 NA 0.1 1
1522 #FF8470 28 6 1 276 27.5 28.5 5.5 6.5 NA 0.1 1
1523 #FFB6B4 28 6 1 276 27.5 28.5 5.5 6.5 NA 0.1 1
1524 #FFC7BB 28 6 1 276 27.5 28.5 5.5 6.5 NA 0.1 1
1525 #FF836F 29 6 1 286 28.5 29.5 5.5 6.5 NA 0.1 1
1526 #FFB6B4 29 6 1 286 28.5 29.5 5.5 6.5 NA 0.1 1
1527 #FFCABB 29 6 1 286 28.5 29.5 5.5 6.5 NA 0.1 1
1528 #FF836E 30 6 1 296 29.5 30.5 5.5 6.5 NA 0.1 1
1529 #FFB6B3 30 6 1 296 29.5 30.5 5.5 6.5 NA 0.1 1
1530 #FFCABB 30 6 1 296 29.5 30.5 5.5 6.5 NA 0.1 1
1531 #FF836E 31 6 1 306 30.5 31.5 5.5 6.5 NA 0.1 1
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1533 #FFCABB 31 6 1 306 30.5 31.5 5.5 6.5 NA 0.1 1
1534 #FF836E 32 6 1 316 31.5 32.5 5.5 6.5 NA 0.1 1
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1557 #BBC8FF 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
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1559 #FFBFBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
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1565 #FFBFBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
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1651 #FF8875 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
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1653 #FFCABB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
1654 #FF8672 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
1655 #FFBFBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
1656 #FFCCBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
1657 #FF8672 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
1658 #FFBFBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
1659 #FFCBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
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1661 #FFBEBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
1662 #FFCABB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
1663 #FF8672 11 8 1 108 10.5 11.5 7.5 8.5 NA 0.1 1
1664 #FFBEBB 11 8 1 108 10.5 11.5 7.5 8.5 NA 0.1 1
1665 #FFC9BB 11 8 1 108 10.5 11.5 7.5 8.5 NA 0.1 1
1666 #FF8875 12 8 1 118 11.5 12.5 7.5 8.5 NA 0.1 1
1667 #FFBDBB 12 8 1 118 11.5 12.5 7.5 8.5 NA 0.1 1
1668 #FFC8BB 12 8 1 118 11.5 12.5 7.5 8.5 NA 0.1 1
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1714 #FF755D 28 8 1 278 27.5 28.5 7.5 8.5 NA 0.1 1
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1716 #FFC2BB 28 8 1 278 27.5 28.5 7.5 8.5 NA 0.1 1
1717 #FF735C 29 8 1 288 28.5 29.5 7.5 8.5 NA 0.1 1
1718 #FFAFAA 29 8 1 288 28.5 29.5 7.5 8.5 NA 0.1 1
1719 #FFC5BB 29 8 1 288 28.5 29.5 7.5 8.5 NA 0.1 1
1720 #FF735B 30 8 1 298 29.5 30.5 7.5 8.5 NA 0.1 1
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1723 #FF725B 31 8 1 308 30.5 31.5 7.5 8.5 NA 0.1 1
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1725 #FFC5BB 31 8 1 308 30.5 31.5 7.5 8.5 NA 0.1 1
1726 #FF725B 32 8 1 318 31.5 32.5 7.5 8.5 NA 0.1 1
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1733 #FFC4BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
1734 #BBCBFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
1735 #FFA8A0 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
1736 #FFC2BB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
1737 #BBCBFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
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1739 #FFC1BB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
1740 #BBCBFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
1741 #FF9181 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
1742 #FFC0BB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
1743 #BBCBFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
1744 #FF8976 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
1745 #FFBFBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
1746 #BBCAFF 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
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1748 #FFBFBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
1749 #BBC9FF 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
1750 #FF826E 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1751 #FFBFBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1752 #BBC6FF 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1753 #FF826E 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
1754 #FFBFBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
1755 #BBC7FF 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
1756 #FF826E 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
1757 #FFBEBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
1758 #BBC9FF 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
1759 #FF826E 11 9 1 109 10.5 11.5 8.5 9.5 NA 0.1 1
1760 #FFBEBB 11 9 1 109 10.5 11.5 8.5 9.5 NA 0.1 1
1761 #BBCAFF 11 9 1 109 10.5 11.5 8.5 9.5 NA 0.1 1
1762 #FF8571 12 9 1 119 11.5 12.5 8.5 9.5 NA 0.1 1
1763 #FFBDBB 12 9 1 119 11.5 12.5 8.5 9.5 NA 0.1 1
1764 #FFC9BB 12 9 1 119 11.5 12.5 8.5 9.5 NA 0.1 1
1765 #FF8571 13 9 1 129 12.5 13.5 8.5 9.5 NA 0.1 1
1766 #FFBDBB 13 9 1 129 12.5 13.5 8.5 9.5 NA 0.1 1
1767 #FFC9BB 13 9 1 129 12.5 13.5 8.5 9.5 NA 0.1 1
1768 #FF8571 14 9 1 139 13.5 14.5 8.5 9.5 NA 0.1 1
1769 #FFBDBB 14 9 1 139 13.5 14.5 8.5 9.5 NA 0.1 1
1770 #FFC8BB 14 9 1 139 13.5 14.5 8.5 9.5 NA 0.1 1
1771 #FF8470 15 9 1 149 14.5 15.5 8.5 9.5 NA 0.1 1
1772 #FFBDBB 15 9 1 149 14.5 15.5 8.5 9.5 NA 0.1 1
1773 #FFC8BB 15 9 1 149 14.5 15.5 8.5 9.5 NA 0.1 1
1774 #FF846F 16 9 1 159 15.5 16.5 8.5 9.5 NA 0.1 1
1775 #FFBDBB 16 9 1 159 15.5 16.5 8.5 9.5 NA 0.1 1
1776 #FFC9BB 16 9 1 159 15.5 16.5 8.5 9.5 NA 0.1 1
1777 #FF806B 17 9 1 169 16.5 17.5 8.5 9.5 NA 0.1 1
1778 #FFBDBB 17 9 1 169 16.5 17.5 8.5 9.5 NA 0.1 1
1779 #FFC9BB 17 9 1 169 16.5 17.5 8.5 9.5 NA 0.1 1
1780 #FF7F6A 18 9 1 179 17.5 18.5 8.5 9.5 NA 0.1 1
1781 #FFBCBB 18 9 1 179 17.5 18.5 8.5 9.5 NA 0.1 1
1782 #FFCCBB 18 9 1 179 17.5 18.5 8.5 9.5 NA 0.1 1
1783 #FF7D68 19 9 1 189 18.5 19.5 8.5 9.5 NA 0.1 1
1784 #FFBBBB 19 9 1 189 18.5 19.5 8.5 9.5 NA 0.1 1
1785 #FFCCBB 19 9 1 189 18.5 19.5 8.5 9.5 NA 0.1 1
1786 #FF7C67 20 9 1 199 19.5 20.5 8.5 9.5 NA 0.1 1
1787 #FFBAB9 20 9 1 199 19.5 20.5 8.5 9.5 NA 0.1 1
1788 #FFCCBB 20 9 1 199 19.5 20.5 8.5 9.5 NA 0.1 1
1789 #FF7B64 21 9 1 209 20.5 21.5 8.5 9.5 NA 0.1 1
1790 #FFB7B5 21 9 1 209 20.5 21.5 8.5 9.5 NA 0.1 1
1791 #FFCCBB 21 9 1 209 20.5 21.5 8.5 9.5 NA 0.1 1
1792 #FF7D67 22 9 1 219 21.5 22.5 8.5 9.5 NA 0.1 1
1793 #FFB4B1 22 9 1 219 21.5 22.5 8.5 9.5 NA 0.1 1
1794 #FFC7BB 22 9 1 219 21.5 22.5 8.5 9.5 NA 0.1 1
1795 #FF7B64 23 9 1 229 22.5 23.5 8.5 9.5 NA 0.1 1
1796 #FFB2AE 23 9 1 229 22.5 23.5 8.5 9.5 NA 0.1 1
1797 #FFC6BB 23 9 1 229 22.5 23.5 8.5 9.5 NA 0.1 1
1798 #FF7A63 24 9 1 239 23.5 24.5 8.5 9.5 NA 0.1 1
1799 #FFB2AE 24 9 1 239 23.5 24.5 8.5 9.5 NA 0.1 1
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1801 #FF7861 25 9 1 249 24.5 25.5 8.5 9.5 NA 0.1 1
1802 #FFB1AD 25 9 1 249 24.5 25.5 8.5 9.5 NA 0.1 1
1803 #FFC6BB 25 9 1 249 24.5 25.5 8.5 9.5 NA 0.1 1
1804 #FF755E 26 9 1 259 25.5 26.5 8.5 9.5 NA 0.1 1
1805 #FFB0AB 26 9 1 259 25.5 26.5 8.5 9.5 NA 0.1 1
1806 #FFC6BB 26 9 1 259 25.5 26.5 8.5 9.5 NA 0.1 1
1807 #FF7058 27 9 1 269 26.5 27.5 8.5 9.5 NA 0.1 1
1808 #FFAFA9 27 9 1 269 26.5 27.5 8.5 9.5 NA 0.1 1
1809 #FFC6BB 27 9 1 269 26.5 27.5 8.5 9.5 NA 0.1 1
1810 #FF6E55 28 9 1 279 27.5 28.5 8.5 9.5 NA 0.1 1
1811 #FFAEA9 28 9 1 279 27.5 28.5 8.5 9.5 NA 0.1 1
1812 #FFC7BB 28 9 1 279 27.5 28.5 8.5 9.5 NA 0.1 1
1813 #FF6C54 29 9 1 289 28.5 29.5 8.5 9.5 NA 0.1 1
1814 #FFAEA9 29 9 1 289 28.5 29.5 8.5 9.5 NA 0.1 1
1815 #FFCABB 29 9 1 289 28.5 29.5 8.5 9.5 NA 0.1 1
1816 #FF6B52 30 9 1 299 29.5 30.5 8.5 9.5 NA 0.1 1
1817 #FFAEA8 30 9 1 299 29.5 30.5 8.5 9.5 NA 0.1 1
1818 #FFCABB 30 9 1 299 29.5 30.5 8.5 9.5 NA 0.1 1
1819 #FF6B52 31 9 1 309 30.5 31.5 8.5 9.5 NA 0.1 1
1820 #FFAEA8 31 9 1 309 30.5 31.5 8.5 9.5 NA 0.1 1
1821 #FFCABB 31 9 1 309 30.5 31.5 8.5 9.5 NA 0.1 1
1822 #FF6B52 32 9 1 319 31.5 32.5 8.5 9.5 NA 0.1 1
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1842 #BBC9FF 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
1843 #FF8571 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
1844 #FFBFBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
1845 #BBC8FF 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
1846 #FF826E 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
1847 #FFBFBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
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1849 #FF826E 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
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1851 #BBC6FF 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
1852 #FF826D 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
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1870 #FF846F 16 10 1 160 15.5 16.5 9.5 10.5 NA 0.1 1
1871 #FFBDBB 16 10 1 160 15.5 16.5 9.5 10.5 NA 0.1 1
1872 #FFC9BB 16 10 1 160 15.5 16.5 9.5 10.5 NA 0.1 1
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1874 #FFBDBB 17 10 1 170 16.5 17.5 9.5 10.5 NA 0.1 1
1875 #FFCABB 17 10 1 170 16.5 17.5 9.5 10.5 NA 0.1 1
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1879 #FF7962 19 10 1 190 18.5 19.5 9.5 10.5 NA 0.1 1
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1882 #FF755E 20 10 1 200 19.5 20.5 9.5 10.5 NA 0.1 1
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1885 #FF6F57 21 10 1 210 20.5 21.5 9.5 10.5 NA 0.1 1
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1893 #FFC6BB 23 10 1 230 22.5 23.5 9.5 10.5 NA 0.1 1
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1896 #FFC6BB 24 10 1 240 23.5 24.5 9.5 10.5 NA 0.1 1
1897 #FF5339 25 10 1 250 24.5 25.5 9.5 10.5 NA 0.1 1
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1901 #FFA49A 26 10 1 260 25.5 26.5 9.5 10.5 NA 0.1 1
1902 #FFC6BB 26 10 1 260 25.5 26.5 9.5 10.5 NA 0.1 1
1903 #FF301A 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1904 #FF9F94 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1905 #FFC6BB 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1906 #FF1106 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1907 #FF9C8F 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1908 #FFC7BB 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1909 #FF0B03 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1910 #FF9C8F 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1911 #FFC9BB 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1912 #FF0100 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1913 #FF9C8F 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1914 #FFCABB 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1915 #FF0000 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1916 #FF9B8F 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1917 #FFCABB 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1918 #FF0000 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1919 #FF9B8F 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1920 #FFCABB 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1921 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1922 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1923 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1924 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1925 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1926 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1927 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1928 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1929 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1930 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1931 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1932 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1933 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1934 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1935 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1936 #BBCCFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1937 #BBCBFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1938 #BBCAFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1939 #FFCABB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1940 #BBCBFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1941 #BBC6FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1942 #FFC9BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
1943 #BBCBFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
1944 #BBC5FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
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83 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
84 #BBC4FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
85 #FFB4B1 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
86 #BBCCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
87 #BBC1FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
88 #FFBDBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
89 #BBCAFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
90 #BBC0FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
91 #FFC2BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
92 #FFC9BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
93 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
94 #FFBEBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
95 #FFC9BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
96 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
97 #FFBDBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
98 #FFC9BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
99 #BBC6FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
100 #FFB2AF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
101 #FFC9BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
102 #BBC3FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
103 #FF826D 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
104 #FFC0BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
105 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
106 #FFA69D 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
107 #FFC0BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
108 #BBC9FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
109 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
110 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
111 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
112 #FF8976 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
113 #FFBDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
114 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
115 #FF745D 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
116 #FFBDBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
117 #BBC5FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
118 #FFB2AE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
119 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
120 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
121 #FF593F 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
122 #FF8A78 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
123 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
124 #FF3A23 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
125 #FF8976 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
126 #FFC7BB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
127 #FFBDBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
128 #FFC7BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
129 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
130 #FF1508 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
131 #FF8B78 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
132 #FFCCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
133 #FF0000 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
134 #FF8976 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
135 #FFCCBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
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19 NA NA
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22 NA NA
23 NA NA
24 NA NA
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26 NA NA
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28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
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96 NA NA
97 NA NA
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99 NA NA
100 NA NA
101 NA NA
102 NA NA
103 NA NA
104 NA NA
105 NA NA
106 NA NA
107 NA NA
108 NA NA
109 NA NA
110 NA NA
111 NA NA
112 NA NA
113 NA NA
114 NA NA
115 NA NA
116 NA NA
117 NA NA
118 NA NA
119 NA NA
120 NA NA
121 NA NA
122 NA NA
123 NA NA
124 NA NA
125 NA NA
126 NA NA
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128 NA NA
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131 NA NA
132 NA NA
133 NA NA
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$.all_cats$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
11 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
12 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
13 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
14 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
15 #BBC8FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
16 #BBCCFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
17 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
18 #BBC8FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
19 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
20 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
22 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
23 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
24 #BBC7FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
25 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
26 #BBC8FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
27 #BBC7FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
28 #FFCABB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
29 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
30 #BBC8FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
31 #BBCAFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
32 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
33 #BBC6FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
34 #BBC9FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
35 #BBC7FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
36 #BBC6FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
37 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
38 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
39 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
40 #BBC8FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
41 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
42 #BBC0FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
43 #BBC7FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
44 #BBC3FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
45 #BBBFFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
46 #FFCABB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
47 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
48 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
49 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
50 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
51 #BBC0FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
52 #BBC7FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
53 #BBC3FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
54 #BBBFFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
55 #FFCABB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
56 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
57 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
58 #BBC9FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
59 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
60 #BBC0FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
61 #BBC6FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
62 #BBC3FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
63 #BBBFFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
64 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
65 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
66 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
67 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
68 #BBC4FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
69 #BBC0FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
70 #BBC6FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
71 #BBC3FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
72 #BBBFFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
73 #FFCABB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
74 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
75 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
76 #BBC9FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
77 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
78 #BBC0FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
79 #BBC7FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
80 #BBC3FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
81 #BBBFFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
82 #FFCABB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
83 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
84 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
85 #BBC9FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
86 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
87 #BBC0FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
88 #BBC7FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
89 #BBC3FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
90 #BBBFFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
91 #FFC3BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
92 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
93 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
94 #FFC1BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
95 #FFCCBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
96 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
97 #FFC0BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
98 #FFCCBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
99 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
100 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
101 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
102 #BBC9FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
103 #FFB2AD 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
104 #FFCCBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
105 #BBC8FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
106 #FFB1AD 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
107 #FFCCBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
108 #BBC8FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
109 #FFB2AD 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
110 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
111 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
112 #FFB2AD 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
113 #FFCCBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
114 #BBC8FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
115 #FFA298 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
116 #FFC9BB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
117 #BBC8FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
118 #FFB2AD 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
119 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
120 #BBC8FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
121 #FFC3BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
122 #FFCCBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
123 #BBCAFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
124 #FFBDBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
125 #FFC9BB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
126 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
127 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
128 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
129 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
130 #FFBDBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
131 #FFCCBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
132 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
133 #FFA298 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
134 #FFC9BB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
135 #BBC8FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
136 #FFB2AD 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
137 #FFCCBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
138 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
139 #FFB2AD 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
140 #FFCCBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
141 #BBC8FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
142 #FFA298 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
143 #FFC9BB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
144 #BBC8FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
145 #FFB2AD 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
146 #FFCCBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
147 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
148 #FFB2AD 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
149 #FFCCBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
150 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
151 #FFBFBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
152 #FFC9BB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
153 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
154 #FFC1BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
155 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
156 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
157 #FFC0BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
158 #FFCCBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
159 #BBCAFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
160 #FFB4B0 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
161 #FFC9BB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
162 #BBC9FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
163 #FFB2AD 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
164 #FFCCBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
165 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
166 #FFB2AD 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
167 #FFCCBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
168 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
169 #FFA298 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
170 #FFC9BB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
171 #BBC8FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
172 #FFB2AD 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
173 #FFCCBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
174 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
175 #FFB2AD 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
176 #FFCCBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
177 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
178 #FFA298 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
179 #FFC9BB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
180 #BBC8FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
181 #FFB3AF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
182 #FFC5BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
183 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
184 #FFABA4 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
185 #FFC5BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
186 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
187 #FFA69D 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
188 #FFC5BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
189 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
190 #FF9B8E 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
191 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
192 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
193 #FF7A64 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
194 #FFC0BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
195 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
196 #FF7A64 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
197 #FFC0BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
198 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
199 #FF8875 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
200 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
201 #BBC9FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
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203 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
204 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
205 #FF735C 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBEBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCCFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFB1AD 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCCFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFB0AB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCCFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFCABB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FFC2BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FFC2BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #BBC9FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FFC2BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBC9FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FFC5BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #BBCCFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #BBCBFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBC9FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FFCBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #BBCBFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #BBC8FF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FFCBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #BBCBFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBC8FF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FFCABB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #BBCCFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #FFC7BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #BBCBFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #BBC9FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #BBCCFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #BBCAFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #BBC8FF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #BBCCFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #BBCAFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #BBC8FF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FFCABB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #BBCBFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #BBCBFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #BBCAFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #BBC8FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #BBCCFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #BBCAFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
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73 #BBCCFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
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75 #BBC8FF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FFCABB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #BBCCFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FFC7BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #BBCBFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBC8FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #BBCBFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #BBCAFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #BBC8FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #BBCCFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #BBCAFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #BBC8FF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #BBCCFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #BBCAFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #BBC8FF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FFCABB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FFC7BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #BBCBFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #BBC8FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #BBCCFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #BBCAFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #BBC8FF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #BBCCFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #BBCAFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #BBC8FF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FFCABB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBC9FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #BBCBFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #BBCAFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #BBC9FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #BBCCFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #BBCAFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #BBC8FF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #BBCCFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #BBCAFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #BBC8FF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FFCABB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #BBCCFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FFC7BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #BBCBFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #BBCBFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #BBCAFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #BBC8FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #BBCCFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #BBCAFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #BBC8FF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #BBCCFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #BBCAFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #BBC8FF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FFCABB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #BBCCFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FFC7BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #BBCBFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #BBCBFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #BBCAFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #BBC8FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #BBCCFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #BBCAFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #BBC8FF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #BBCCFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #BBCAFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #BBC8FF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
151 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
152 #FFC9BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
153 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
154 #FFA69C 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
155 #FFC9BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
156 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
157 #FF9C8F 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
158 #FFC9BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
159 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
160 #FFC6BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
161 #FFC9BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
162 #BBCCFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
163 #FF9A8D 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
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$.all_cats$eff
NULL
Code
capture.output(print(cat_cars_ale))
Message
<ALE> object of a <multinom/nnet> model that predicts `continent` (a
categorical outcome) from a 64-row by 8-column dataset.
ALE data, statistics, surrogate p-values, and raw bootstrap data are provided
for the following terms:
7 1D terms: mpg, vs, am, model, gear, carb, and wt
21 2D terms: mpg:vs, mpg:am, mpg:model, mpg:gear, mpg:carb, mpg:wt, vs:am,
vs:model, vs:gear, vs:carb, vs:wt, am:model, am:gear, am:carb, am:wt,
model:gear, model:carb, model:wt, ..., gear:wt, and carb:wt
The results were bootstrapped with 2 iterations.
Output
character(0)
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ale documentation built on April 11, 2025, 6:09 p.m.
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bootstrapped binary outcome with full 1D and 2D ALE
Code
unclass(cat_cars_ale)
Output
<object>
attr(,"S7_class")
<ale::ALE> class
@ parent : <S7_object>
@ constructor: function(model, x_cols, data, y_col, ..., exclude_cols, parallel, model_packages, output_stats, output_boot_data, pred_fun, pred_type, p_values, aler_alpha, max_num_bins, boot_it, boot_alpha, boot_centre, seed, y_type, sample_size, silent, .bins) {...}
@ validator : <NULL>
@ properties :
$ effect: <list>
$ params: <list>
attr(,"effect")
attr(,"effect")$Asia
attr(,"effect")$Asia$ale
attr(,"effect")$Asia$ale$d1
attr(,"effect")$Asia$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$Asia$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
attr(,"effect")$Asia$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
attr(,"effect")$Asia$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
attr(,"effect")$Asia$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
attr(,"effect")$Asia$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
attr(,"effect")$Asia$ale$d2
attr(,"effect")$Asia$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$Asia$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$Asia$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$Asia$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$Asia$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$Asia$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$Asia$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
attr(,"effect")$Asia$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
attr(,"effect")$Asia$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
attr(,"effect")$Asia$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
attr(,"effect")$Asia$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
attr(,"effect")$Asia$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
attr(,"effect")$Asia$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
attr(,"effect")$Asia$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
attr(,"effect")$Asia$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
attr(,"effect")$Asia$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
attr(,"effect")$Asia$stats
attr(,"effect")$Asia$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$Asia$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$Asia$boot_data
attr(,"effect")$Asia$boot_data$d1
attr(,"effect")$Asia$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$Asia$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$Asia$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE -0.0432 38 -0.0432 -0.0432
2 0 TRUE 0.0632 26 0.0632 0.0632
3 1 FALSE 0.0321 38 0.0321 0.0321
4 1 TRUE -0.0101 26 -0.0101 -0.0101
5 2 FALSE 0.0297 38 0.0297 0.0297
6 2 TRUE -0.00670 26 -0.00670 -0.00670
attr(,"effect")$Asia$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental -0.305 2 -0.305 -0.305
2 0 Cadillac Fleetwood -0.305 2 -0.305 -0.305
3 0 Merc 450SL -0.305 2 -0.305 -0.305
4 0 Merc 450SLC -0.305 2 -0.305 -0.305
5 0 Camaro Z28 -0.305 2 -0.305 -0.305
6 0 Chrysler Imperial -0.305 2 -0.305 -0.305
7 0 Duster 360 -0.305 2 -0.305 -0.305
8 0 Merc 450SE -0.305 2 -0.305 -0.305
9 0 Hornet Sportabout -0.305 2 -0.305 -0.305
10 0 AMC Javelin -0.305 2 -0.305 -0.305
# i 86 more rows
attr(,"effect")$Asia$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.118 30 0.118 0.118
2 0 four 0.0718 24 0.0718 0.0718
3 0 five -0.526 10 -0.526 -0.526
4 1 three 0.0530 30 0.0530 0.0530
5 1 four -0.162 24 -0.162 -0.162
6 1 five -0.460 10 -0.460 -0.460
7 2 three 0.184 30 0.184 0.184
8 2 four 0.0960 24 0.0960 0.0960
9 2 five -0.00410 10 -0.00410 -0.00410
attr(,"effect")$Asia$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 0.00549 14 0.00549 0.00549
2 0 2 -0.00191 19 -0.00191 -0.00191
3 0 3 -0.00191 9 -0.00191 -0.00191
4 0 4 -0.00191 16 -0.00191 -0.00191
5 0 8 -0.00191 6 -0.00191 -0.00191
6 1 1 0.00549 18 0.00549 0.00549
7 1 2 -0.00377 16 -0.00377 -0.00377
8 1 3 -0.00377 13 -0.00377 -0.00377
9 1 4 -0.00377 15 -0.00377 -0.00377
10 1 8 -0.00377 2 -0.00377 -0.00377
11 2 1 0.00549 15 0.00549 0.00549
12 2 2 -0.0000232 13 -0.0000232 -0.0000232
13 2 3 -0.0000232 13 -0.0000232 -0.0000232
14 2 4 -0.0000232 20 -0.0000232 -0.0000232
15 2 8 -0.0000232 3 -0.0000232 -0.0000232
attr(,"effect")$Asia$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 0.0000319 1 0.0000319 0.0000319
2 0 1.94 0.00000854 7 0.00000854 0.00000854
3 0 2.46 -0.00000364 7 -0.00000364 -0.00000364
4 0 2.79 -0.00000366 7 -0.00000366 -0.00000366
5 0 3.19 -0.00000366 7 -0.00000366 -0.00000366
6 0 3.44 -0.00000366 7 -0.00000366 -0.00000366
7 0 3.52 -0.00000366 7 -0.00000366 -0.00000366
8 0 3.73 -0.00000366 7 -0.00000366 -0.00000366
9 0 4.05 -0.00000366 7 -0.00000366 -0.00000366
10 0 5.45 -0.00000366 7 -0.00000366 -0.00000366
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2
attr(,"effect")$Asia$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$Asia$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$Asia$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$Asia$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0.106 2 0.106 0.106
2 0 TRUE Lincoln Continental 0.106 0 -1.38 -1.38
3 0 FALSE Cadillac Fleetwood 0.106 2 0.106 0.106
4 0 TRUE Cadillac Fleetwood 0.106 0 -1.38 -1.38
5 0 FALSE Merc 450SL 0.106 2 0.106 0.106
6 0 TRUE Merc 450SL 0.106 0 -1.38 -1.38
7 0 FALSE Merc 450SLC 0.106 2 0.106 0.106
8 0 TRUE Merc 450SLC 0.106 0 -1.38 -1.38
9 0 FALSE Camaro Z28 0.106 2 0.106 0.106
10 0 TRUE Camaro Z28 0.106 0 -1.38 -1.38
# i 182 more rows
attr(,"effect")$Asia$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.0688 30 -0.0688 -0.0688
2 0 TRUE three -0.0688 0 0.453 0.453
3 0 FALSE four -0.0688 8 -0.0688 -0.0688
4 0 TRUE four -0.466 16 0.0563 0.0563
5 0 FALSE five -0.0688 0 -0.0688 -0.0688
6 0 TRUE five -1.06 10 -0.541 -0.541
7 1 FALSE three -0.204 30 -0.204 -0.204
8 1 TRUE three -0.204 0 -0.104 -0.104
9 1 FALSE four -0.340 8 -0.340 -0.340
10 1 TRUE four -0.607 16 -0.507 -0.507
11 1 FALSE five -0.340 0 -0.340 -0.340
12 1 TRUE five -0.905 10 -0.806 -0.806
13 2 FALSE three -0.00554 30 -0.00554 -0.00554
14 2 TRUE three -0.00554 0 -0.628 -0.628
15 2 FALSE four -0.165 8 -0.165 -0.165
16 2 TRUE four -0.228 16 -0.850 -0.850
17 2 FALSE five -0.165 0 -0.165 -0.165
18 2 TRUE five -0.328 10 -0.950 -0.950
attr(,"effect")$Asia$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0.0166 6 0.0166 0.0166
2 0 TRUE 1 0.0166 8 -0.107 -0.107
3 0 FALSE 2 0.0166 12 -0.0662 -0.0662
4 0 TRUE 2 -0.00350 7 -0.213 -0.213
5 0 FALSE 3 0.0166 7 0.0203 0.0203
6 0 TRUE 3 -0.00342 2 -0.127 -0.127
7 0 FALSE 4 0.0166 11 -0.0662 -0.0662
8 0 TRUE 4 0.550 5 0.423 0.423
9 0 FALSE 8 0.0166 2 0.0203 0.0203
10 0 TRUE 8 0.550 4 0.340 0.340
# i 20 more rows
attr(,"effect")$Asia$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0.0139 0 0.0139 0.0139
2 0 TRUE 1.50 0.0139 1 -0.00322 -0.00322
3 0 FALSE 1.94 0.0139 0 0.00361 0.00361
4 0 TRUE 1.94 0.0138 7 -0.0244 -0.0244
5 0 FALSE 2.46 0.0139 1 -0.00732 -0.00732
6 0 TRUE 2.46 0.0138 6 -0.0245 -0.0245
7 0 FALSE 2.79 0.0139 1 -0.00732 -0.00732
8 0 TRUE 2.79 0.0378 6 0.0207 0.0207
9 0 FALSE 3.19 0.0139 3 0.0139 0.0139
10 0 TRUE 3.19 0.0761 4 0.0487 0.0487
# i 50 more rows
attr(,"effect")$Asia$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.184 2 -0.184 -0.184
2 0 Cadillac Fleetwood three -0.184 2 -0.184 -0.184
3 0 Merc 450SL three -0.184 2 -0.184 -0.184
4 0 Merc 450SLC three -0.184 2 -0.184 -0.184
5 0 Camaro Z28 three -0.184 2 -0.184 -0.184
6 0 Chrysler Imperial three -0.184 2 0.274 0.274
7 0 Duster 360 three -0.184 2 0.272 0.272
8 0 Merc 450SE three -0.184 2 0.250 0.250
9 0 Hornet Sportabout three -0.184 2 0.250 0.250
10 0 AMC Javelin three -0.184 2 -0.0198 -0.0198
# i 278 more rows
attr(,"effect")$Asia$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 3.42 0 3.42 3.42
2 0 Cadillac Fleetwood 1 3.42 0 3.42 3.42
3 0 Merc 450SL 1 3.42 0 3.42 3.42
4 0 Merc 450SLC 1 3.42 0 3.42 3.42
5 0 Camaro Z28 1 3.42 0 3.42 3.42
6 0 Chrysler Imperial 1 3.42 0 3.00 3.00
7 0 Duster 360 1 3.42 0 -0.418 -0.418
8 0 Merc 450SE 1 3.42 0 -3.32 -3.32
9 0 Hornet Sportabout 1 3.42 0 -3.74 -3.74
10 0 AMC Javelin 1 3.42 0 -4.12 -4.12
# i 470 more rows
attr(,"effect")$Asia$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.342 0 0.342 0.342
2 0 Cadillac Fleetwood 1.50 0.342 0 0.342 0.342
3 0 Merc 450SL 1.50 0.342 0 0.342 0.342
4 0 Merc 450SLC 1.50 0.342 0 0.342 0.342
5 0 Camaro Z28 1.50 0.342 0 0.342 0.342
6 0 Chrysler Imperial 1.50 0.342 0 0.342 0.342
7 0 Duster 360 1.50 0.342 0 0.284 0.284
8 0 Merc 450SE 1.50 0.342 0 -0.0324 -0.0324
9 0 Hornet Sportabout 1.50 0.342 0 -0.0439 -0.0439
10 0 AMC Javelin 1.50 0.342 0 -0.0520 -0.0520
# i 950 more rows
attr(,"effect")$Asia$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.0284 6 0.0284 0.0284
2 0 four 1 0.0284 8 -0.0211 -0.0211
3 0 five 1 0.0284 0 -0.0245 -0.0245
4 0 three 2 0.0284 8 -0.0766 -0.0766
5 0 four 2 0.162 7 0.0573 0.0573
6 0 five 2 0.130 4 0.130 0.130
7 0 three 3 0.0284 7 -0.0211 -0.0211
8 0 four 3 0.193 2 0.140 0.140
9 0 five 3 0.147 0 0.0422 0.0422
10 0 three 4 0.0284 8 -0.0767 -0.0767
# i 35 more rows
attr(,"effect")$Asia$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 0.0363 0 0.0363 0.0363
2 0 four 1.50 0.0363 0 0.0269 0.0269
3 0 five 1.50 0.0363 1 0.0202 0.0202
4 0 three 1.94 0.0363 0 0.00815 0.00815
5 0 four 1.94 0.0527 6 0.00226 0.00226
6 0 five 1.94 0.0535 1 0.00298 0.00298
7 0 three 2.46 0.0363 1 -0.0141 -0.0141
8 0 four 2.46 0.0599 4 0.00943 0.00943
9 0 five 2.46 0.0786 2 0.0281 0.0281
10 0 three 2.79 0.0363 1 -0.0141 -0.0141
# i 80 more rows
attr(,"effect")$Asia$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.00454 0 0.00454 0.00454
2 0 2 1.50 0.00454 1 -0.0287 -0.0287
3 0 3 1.50 0.00454 0 -0.0230 -0.0230
4 0 4 1.50 0.00454 0 -0.0329 -0.0329
5 0 8 1.50 0.00454 0 -0.0335 -0.0335
6 0 1 1.94 0.00454 4 -0.00377 -0.00377
7 0 2 1.94 0.0244 3 -0.0171 -0.0171
8 0 3 1.94 0.0244 0 -0.00721 -0.00721
9 0 4 1.94 0.0244 0 -0.0171 -0.0171
10 0 8 1.94 0.0244 0 -0.0171 -0.0171
# i 140 more rows
attr(,"effect")$Europe
attr(,"effect")$Europe$ale
attr(,"effect")$Europe$ale$d1
attr(,"effect")$Europe$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$Europe$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
attr(,"effect")$Europe$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
attr(,"effect")$Europe$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
attr(,"effect")$Europe$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
attr(,"effect")$Europe$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
attr(,"effect")$Europe$ale$d2
attr(,"effect")$Europe$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$Europe$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$Europe$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$Europe$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$Europe$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$Europe$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$Europe$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
attr(,"effect")$Europe$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
attr(,"effect")$Europe$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
attr(,"effect")$Europe$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
attr(,"effect")$Europe$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
attr(,"effect")$Europe$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
attr(,"effect")$Europe$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
attr(,"effect")$Europe$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
attr(,"effect")$Europe$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
attr(,"effect")$Europe$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
attr(,"effect")$Europe$stats
attr(,"effect")$Europe$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$Europe$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$Europe$boot_data
attr(,"effect")$Europe$boot_data$d1
attr(,"effect")$Europe$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$Europe$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$Europe$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0.0432 38 0.0432 0.0432
2 0 TRUE -0.0632 26 -0.0632 -0.0632
3 1 FALSE -0.0321 38 -0.0321 -0.0321
4 1 TRUE 0.0101 26 0.0101 0.0101
5 2 FALSE -0.0297 38 -0.0297 -0.0297
6 2 TRUE 0.00670 26 0.00670 0.00670
attr(,"effect")$Europe$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.166 2 0.166 0.166
2 0 Cadillac Fleetwood 0.166 2 0.166 0.166
3 0 Merc 450SL 1.17 2 1.17 1.17
4 0 Merc 450SLC 1.17 2 1.17 1.17
5 0 Camaro Z28 0.166 2 0.166 0.166
6 0 Chrysler Imperial 0.166 2 0.166 0.166
7 0 Duster 360 0.166 2 0.166 0.166
8 0 Merc 450SE 1.17 2 1.17 1.17
9 0 Hornet Sportabout 0.166 2 0.166 0.166
10 0 AMC Javelin 0.166 2 0.166 0.166
# i 86 more rows
attr(,"effect")$Europe$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three -0.372 30 -0.372 -0.372
2 0 four 0.211 24 0.211 0.211
3 0 five 0.610 10 0.610 0.610
4 1 three -0.106 30 -0.106 -0.106
5 1 four 0.394 24 0.394 0.394
6 1 five 0.594 10 0.594 0.594
7 2 three -0.239 30 -0.239 -0.239
8 2 four 0.136 24 0.136 0.136
9 2 five -0.0638 10 -0.0638 -0.0638
attr(,"effect")$Europe$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 -0.00549 14 -0.00549 -0.00549
2 0 2 0.00191 19 0.00191 0.00191
3 0 3 0.00191 9 0.00191 0.00191
4 0 4 0.00191 16 0.00191 0.00191
5 0 8 0.00191 6 0.00191 0.00191
6 1 1 -0.00549 18 -0.00549 -0.00549
7 1 2 0.00377 16 0.00377 0.00377
8 1 3 0.00377 13 0.00377 0.00377
9 1 4 0.00377 15 0.00377 0.00377
10 1 8 0.00377 2 0.00377 0.00377
11 2 1 -0.00549 15 -0.00549 -0.00549
12 2 2 0.0000232 13 0.0000232 0.0000232
13 2 3 0.0000232 13 0.0000232 0.0000232
14 2 4 0.0000232 20 0.0000232 0.0000232
15 2 8 0.0000232 3 0.0000232 0.0000232
attr(,"effect")$Europe$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 -0.0000319 1 -0.0000319 -0.0000319
2 0 1.94 -0.00000854 7 -0.00000854 -0.00000854
3 0 2.46 0.00000364 7 0.00000364 0.00000364
4 0 2.79 0.00000366 7 0.00000366 0.00000366
5 0 3.19 0.00000366 7 0.00000366 0.00000366
6 0 3.44 0.00000366 7 0.00000366 0.00000366
7 0 3.52 0.00000366 7 0.00000366 0.00000366
8 0 3.73 0.00000366 7 0.00000366 0.00000366
9 0 4.05 0.00000366 7 0.00000366 0.00000366
10 0 5.45 0.00000366 7 0.00000366 0.00000366
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2
attr(,"effect")$Europe$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$Europe$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$Europe$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$Europe$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0844 2 -1.57 -1.57
2 0 TRUE Lincoln Continental -0.0844 0 -0.0844 -0.0844
3 0 FALSE Cadillac Fleetwood -0.0844 2 -1.57 -1.57
4 0 TRUE Cadillac Fleetwood -0.0844 0 -0.0844 -0.0844
5 0 FALSE Merc 450SL -0.0844 2 -1.57 -1.57
6 0 TRUE Merc 450SL -0.0844 0 -0.0844 -0.0844
7 0 FALSE Merc 450SLC -0.0844 2 -1.57 -1.57
8 0 TRUE Merc 450SLC -0.0844 0 -0.0844 -0.0844
9 0 FALSE Camaro Z28 -0.0844 2 -1.57 -1.57
10 0 TRUE Camaro Z28 -0.0844 0 -0.0844 -0.0844
# i 182 more rows
attr(,"effect")$Europe$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.00407 30 0.386 0.386
2 0 TRUE three -0.00407 0 0.427 0.427
3 0 FALSE four -0.00407 8 0.386 0.386
4 0 TRUE four -0.177 16 0.254 0.254
5 0 FALSE five -0.00407 0 0.386 0.386
6 0 TRUE five 0.226 10 0.657 0.657
7 1 FALSE three -0.0677 30 -0.0254 -0.0254
8 1 TRUE three -0.0677 0 -0.212 -0.212
9 1 FALSE four -0.187 8 -0.145 -0.145
10 1 TRUE four -0.241 16 -0.385 -0.385
11 1 FALSE five -0.187 0 -0.145 -0.145
12 1 TRUE five -0.0395 10 -0.183 -0.183
13 2 FALSE three -0.0677 30 -0.500 -0.500
14 2 TRUE three -0.0677 0 -0.355 -0.355
15 2 FALSE four -0.176 8 -0.608 -0.608
16 2 TRUE four -0.175 16 -0.463 -0.463
17 2 FALSE five -0.176 0 -0.608 -0.608
18 2 TRUE five -0.0753 10 -0.363 -0.363
attr(,"effect")$Europe$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0166 6 -0.144 -0.144
2 0 TRUE 1 -0.0166 8 -0.0129 -0.0129
3 0 FALSE 2 -0.0166 12 -0.226 -0.226
4 0 TRUE 2 0.00350 7 0.00350 0.00350
5 0 FALSE 3 -0.0166 7 -0.140 -0.140
6 0 TRUE 3 0.00342 2 -0.0793 -0.0793
7 0 FALSE 4 -0.0166 11 -0.226 -0.226
8 0 TRUE 4 -0.550 5 -0.546 -0.546
9 0 FALSE 8 -0.0166 2 -0.140 -0.140
10 0 TRUE 8 -0.550 4 -0.633 -0.633
# i 20 more rows
attr(,"effect")$Europe$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 -0.0139 0 -0.0310 -0.0310
2 0 TRUE 1.50 -0.0139 1 -0.0138 -0.0138
3 0 FALSE 1.94 -0.0139 0 -0.0412 -0.0412
4 0 TRUE 1.94 -0.0138 7 -0.0350 -0.0350
5 0 FALSE 2.46 -0.0139 1 -0.0522 -0.0522
6 0 TRUE 2.46 -0.0138 6 -0.0350 -0.0350
7 0 FALSE 2.79 -0.0139 1 -0.0522 -0.0522
8 0 TRUE 2.79 -0.0378 6 -0.0378 -0.0378
9 0 FALSE 3.19 -0.0139 3 -0.0309 -0.0309
10 0 TRUE 3.19 -0.0761 4 -0.0973 -0.0973
# i 50 more rows
attr(,"effect")$Europe$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three 0.301 2 0.301 0.301
2 0 Cadillac Fleetwood three 0.301 2 0.301 0.301
3 0 Merc 450SL three 0.301 2 0.301 0.301
4 0 Merc 450SLC three 0.301 2 0.301 0.301
5 0 Camaro Z28 three 0.301 2 0.301 0.301
6 0 Chrysler Imperial three 0.301 2 0.759 0.759
7 0 Duster 360 three 0.301 2 0.729 0.729
8 0 Merc 450SE three 0.301 2 0.235 0.235
9 0 Hornet Sportabout three 0.301 2 0.794 0.794
10 0 AMC Javelin three 0.301 2 0.964 0.964
# i 278 more rows
attr(,"effect")$Europe$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 -3.89 0 -3.89 -3.89
2 0 Cadillac Fleetwood 1 -3.89 0 -3.89 -3.89
3 0 Merc 450SL 1 -3.89 0 -3.89 -3.89
4 0 Merc 450SLC 1 -3.89 0 -3.89 -3.89
5 0 Camaro Z28 1 -3.89 0 -3.89 -3.89
6 0 Chrysler Imperial 1 -3.89 0 -5.45 -5.45
7 0 Duster 360 1 -3.89 0 -8.67 -8.67
8 0 Merc 450SE 1 -3.89 0 -11.1 -11.1
9 0 Hornet Sportabout 1 -3.89 0 -11.1 -11.1
10 0 AMC Javelin 1 -3.89 0 -11.4 -11.4
# i 470 more rows
attr(,"effect")$Europe$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 -0.403 0 -0.403 -0.403
2 0 Cadillac Fleetwood 1.50 -0.403 0 -0.403 -0.403
3 0 Merc 450SL 1.50 -0.403 0 -0.403 -0.403
4 0 Merc 450SLC 1.50 -0.403 0 -0.403 -0.403
5 0 Camaro Z28 1.50 -0.403 0 -0.403 -0.403
6 0 Chrysler Imperial 1.50 -0.403 0 -0.460 -0.460
7 0 Duster 360 1.50 -0.403 0 -0.555 -0.555
8 0 Merc 450SE 1.50 -0.403 0 -0.778 -0.778
9 0 Hornet Sportabout 1.50 -0.403 0 -0.789 -0.789
10 0 AMC Javelin 1.50 -0.403 0 -0.797 -0.797
# i 950 more rows
attr(,"effect")$Europe$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.00218 6 -0.105 -0.105
2 0 four 1 0.00218 8 -0.155 -0.155
3 0 five 1 0.00218 0 -0.158 -0.158
4 0 three 2 0.00218 8 -0.210 -0.210
5 0 four 2 0.00237 7 -0.210 -0.210
6 0 five 2 0.0348 4 -0.0727 -0.0727
7 0 three 3 0.00218 7 -0.155 -0.155
8 0 four 3 0.00237 2 -0.158 -0.158
9 0 five 3 0.0510 0 -0.161 -0.161
10 0 three 4 0.00218 8 -0.210 -0.210
# i 35 more rows
attr(,"effect")$Europe$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.00213 0 -0.0423 -0.0423
2 0 four 1.50 -0.00213 0 -0.0517 -0.0517
3 0 five 1.50 -0.00213 1 -0.0584 -0.0584
4 0 three 1.94 -0.00213 0 -0.0705 -0.0705
5 0 four 1.94 -0.00210 6 -0.0927 -0.0927
6 0 five 1.94 -0.00282 1 -0.0935 -0.0935
7 0 three 2.46 -0.00213 1 -0.0928 -0.0928
8 0 four 2.46 -0.00208 4 -0.0927 -0.0927
9 0 five 2.46 -0.0208 2 -0.111 -0.111
10 0 three 2.79 -0.00213 1 -0.0928 -0.0928
# i 80 more rows
attr(,"effect")$Europe$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 -0.00454 0 -0.0278 -0.0278
2 0 2 1.50 -0.00454 1 -0.0377 -0.0377
3 0 3 1.50 -0.00454 0 -0.0420 -0.0420
4 0 4 1.50 -0.00454 0 -0.00941 -0.00941
5 0 8 1.50 -0.00454 0 -0.0426 -0.0426
6 0 1 1.94 -0.00454 4 -0.0361 -0.0361
7 0 2 1.94 -0.0244 3 -0.0659 -0.0659
8 0 3 1.94 -0.0244 0 -0.0659 -0.0659
9 0 4 1.94 -0.0244 0 -0.0327 -0.0327
10 0 8 1.94 -0.0244 0 -0.0659 -0.0659
# i 140 more rows
attr(,"effect")$`North America`
attr(,"effect")$`North America`$ale
attr(,"effect")$`North America`$ale$d1
attr(,"effect")$`North America`$ale$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0
2 14.4 7 0 0 0 0 0
3 15.2 7 0 0 0 0 0
4 16.4 7 0 0 0 0 0
5 18.7 7 0 0 0 0 0
6 19.8 7 0 0 0 0 0
7 21.4 7 0 0 0 0 0
8 22.9 7 0 0 0 0 0
9 30.1 7 0 0 0 0 0
10 33.9 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 0 0 0 0 0
2 TRUE 28 0 0 0 0 0
attr(,"effect")$`North America`$ale$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.72e-11 7.00e-10
attr(,"effect")$`North America`$ale$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
attr(,"effect")$`North America`$ale$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
attr(,"effect")$`North America`$ale$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
attr(,"effect")$`North America`$ale$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.50e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.29e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.09e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.89e-11 6.09e-10
attr(,"effect")$`North America`$ale$d2
attr(,"effect")$`North America`$ale$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 7 0 0 0 0 0
4 16.4 FALSE 7 0 0 0 0 0
5 18.7 FALSE 3 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 4 0 0 0 0 0
8 22.9 FALSE 0 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 0 0 0 0 0 0
14 16.4 TRUE 0 0 0 0 0 0
15 18.7 TRUE 4 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 3 0 0 0 0 0
18 22.9 TRUE 7 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 0 0 0 0 0
2 14.4 FALSE 7 0 0 0 0 0
3 15.2 FALSE 5 0 0 0 0 0
4 16.4 FALSE 5 0 0 0 0 0
5 18.7 FALSE 7 0 0 0 0 0
6 19.8 FALSE 5 0 0 0 0 0
7 21.4 FALSE 1 0 0 0 0 0
8 22.9 FALSE 5 0 0 0 0 0
9 30.1 FALSE 2 0 0 0 0 0
10 33.9 FALSE 0 0 0 0 0 0
11 10.4 TRUE 0 0 0 0 0 0
12 14.4 TRUE 0 0 0 0 0 0
13 15.2 TRUE 2 0 0 0 0 0
14 16.4 TRUE 2 0 0 0 0 0
15 18.7 TRUE 0 0 0 0 0 0
16 19.8 TRUE 2 0 0 0 0 0
17 21.4 TRUE 6 0 0 0 0 0
18 22.9 TRUE 2 0 0 0 0 0
19 30.1 TRUE 5 0 0 0 0 0
20 33.9 TRUE 7 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Continental 0 0 0 0 0 0
2 14.4 Lincoln Continental 2 0 0 0 0 0
3 15.2 Lincoln Continental 0 0 0 0 0 0
4 16.4 Lincoln Continental 0 0 0 0 0 0
5 18.7 Lincoln Continental 0 0 0 0 0 0
6 19.8 Lincoln Continental 0 0 0 0 0 0
7 21.4 Lincoln Continental 0 0 0 0 0 0
8 22.9 Lincoln Continental 0 0 0 0 0 0
9 30.1 Lincoln Continental 0 0 0 0 0 0
10 33.9 Lincoln Continental 0 0 0 0 0 0
# i 310 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 0 0 0 0 0
2 14.4 three 7 0 0 0 0 0
3 15.2 three 5 0 0 0 0 0
4 16.4 three 5 0 0 0 0 0
5 18.7 three 5 0 0 0 0 0
6 19.8 three 3 0 0 0 0 0
7 21.4 three 1 0 0 0 0 0
8 22.9 three 3 0 0 0 0 0
9 30.1 three 0 0 0 0 0 0
10 33.9 three 0 0 0 0 0 0
# i 20 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 0 0 0 0 0
2 14.4 1 0 0 0 0 0 0
3 15.2 1 0 0 0 0 0 0
4 16.4 1 0 0 0 0 0 0
5 18.7 1 2 0 0 0 0 0
6 19.8 1 0 0 0 0 0 0
7 21.4 1 1 0 0 0 0 0
8 22.9 1 5 0 0 0 0 0
9 30.1 1 2 0 0 0 0 0
10 33.9 1 4 0 0 0 0 0
# i 40 more rows
attr(,"effect")$`North America`$ale$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 0 0 0 0 0
2 14.4 1.50 0 0 0 0 0 0
3 15.2 1.50 0 0 0 0 0 0
4 16.4 1.50 0 0 0 0 0 0
5 18.7 1.50 0 0 0 0 0 0
6 19.8 1.50 0 0 0 0 0 0
7 21.4 1.50 0 0 0 0 0 0
8 22.9 1.50 0 0 0 0 0 0
9 30.1 1.50 1 0 0 0 0 0
10 33.9 1.50 0 0 0 0 0 0
# i 90 more rows
attr(,"effect")$`North America`$ale$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 0 0 0 0 0
2 TRUE FALSE 14 0 0 0 0 0
3 FALSE TRUE 12 0 0 0 0 0
4 TRUE TRUE 14 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0 0 0 0 0
2 TRUE Lincoln Continental 0 0 0 0 0 0
3 FALSE Cadillac Fleetwood 2 0 0 0 0 0
4 TRUE Cadillac Fleetwood 0 0 0 0 0 0
5 FALSE Merc 450SL 2 0 0 0 0 0
6 TRUE Merc 450SL 0 0 0 0 0 0
7 FALSE Merc 450SLC 2 0 0 0 0 0
8 TRUE Merc 450SLC 0 0 0 0 0 0
9 FALSE Camaro Z28 2 0 0 0 0 0
10 TRUE Camaro Z28 0 0 0 0 0 0
# i 54 more rows
attr(,"effect")$`North America`$ale$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 0 0 0 0 0
2 TRUE three 6 0 0 0 0 0
3 FALSE four 4 0 0 0 0 0
4 TRUE four 20 0 0 0 0 0
5 FALSE five 8 0 0 0 0 0
6 TRUE five 2 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 0 0 0 0 0
2 TRUE 1 14 0 0 0 0 0
3 FALSE 2 10 0 0 0 0 0
4 TRUE 2 9 0 0 0 0 0
5 FALSE 3 8 0 0 0 0 0
6 TRUE 3 1 0 0 0 0 0
7 FALSE 4 13 0 0 0 0 0
8 TRUE 4 3 0 0 0 0 0
9 FALSE 8 5 0 0 0 0 0
10 TRUE 8 1 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0 0 0 0 0
2 TRUE 1.50 1 0 0 0 0 0
3 FALSE 1.94 0 0 0 0 0 0
4 TRUE 1.94 7 0 0 0 0 0
5 FALSE 2.46 2 0 0 0 0 0
6 TRUE 2.46 5 0 0 0 0 0
7 FALSE 2.79 4 0 0 0 0 0
8 TRUE 2.79 3 0 0 0 0 0
9 FALSE 3.19 4 0 0 0 0 0
10 TRUE 3.19 3 0 0 0 0 0
11 FALSE 3.44 2 0 0 0 0 0
12 TRUE 3.44 5 0 0 0 0 0
13 FALSE 3.52 3 0 0 0 0 0
14 TRUE 3.52 4 0 0 0 0 0
15 FALSE 3.73 7 0 0 0 0 0
16 TRUE 3.73 0 0 0 0 0 0
17 FALSE 4.05 7 0 0 0 0 0
18 TRUE 4.05 0 0 0 0 0 0
19 FALSE 5.45 7 0 0 0 0 0
20 TRUE 5.45 0 0 0 0 0 0
attr(,"effect")$`North America`$ale$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
2 TRUE Lincoln Continental 0 -0.0218 -1.43 -0.0218 0.0596 1.32
3 FALSE Cadillac Fleetwood 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
4 TRUE Cadillac Fleetwood 0 -0.0218 -1.43 -0.0218 0.0596 1.32
5 FALSE Merc 450SL 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
6 TRUE Merc 450SL 0 -0.0226 -1.43 -0.0226 0.0596 1.31
7 FALSE Merc 450SLC 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
8 TRUE Merc 450SLC 0 -0.0226 -1.43 -0.0226 0.0596 1.31
9 FALSE Camaro Z28 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
10 TRUE Camaro Z28 0 -0.0226 -1.43 -0.0226 0.0596 1.31
# i 54 more rows
attr(,"effect")$`North America`$ale$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 0.139 -0.101 0.139 0.205 0.323
2 TRUE three 0 0.139 -0.605 0.139 0.170 0.858
3 FALSE four 8 0.313 0.153 0.313 0.205 0.566
4 TRUE four 16 0.631 -0.263 0.631 0.746 1.43
5 FALSE five 0 0.313 0.153 0.313 0.205 0.566
6 TRUE five 10 0.728 -0.259 0.728 0.843 1.62
attr(,"effect")$`North America`$ale$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0000000460 -0.00351 -4.60e-8 2.63e-8 3.51e-3
2 TRUE 1 8 -0.0000000460 -0.117 -4.60e-8 2.47e-7 1.17e-1
3 FALSE 2 12 -0.000000106 -0.0786 -1.06e-7 2.31e-8 7.86e-2
4 TRUE 2 7 -0.000000373 -0.121 -3.73e-7 -5.45e-8 1.21e-1
5 FALSE 3 7 -0.000000106 -0.00351 -1.06e-7 2.19e-8 3.50e-3
6 TRUE 3 2 -0.000000373 -0.199 -3.73e-7 1.90e-8 1.99e-1
7 FALSE 4 11 -0.000000109 -0.000000220 -1.09e-7 -5.33e-8 -4.64e-8
8 TRUE 4 5 -0.000000380 -0.117 -3.80e-7 1.25e-8 1.17e-1
9 FALSE 8 2 -0.000000110 -0.0786 -1.10e-7 1.95e-8 7.86e-2
10 TRUE 8 4 -0.000000380 -0.199 -3.80e-7 1.30e-8 1.99e-1
attr(,"effect")$`North America`$ale$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 9.93e-11 -1.11e- 5 9.93e-11 4.34e-10 0.0000111
2 TRUE 1.50 1 9.93e-11 -1.62e- 2 9.93e-11 5.51e-10 0.0162
3 FALSE 1.94 0 9.85e-11 -2.01e- 2 9.85e-11 -1.47e-10 0.0201
4 TRUE 1.94 7 -4.37e-10 -3.64e- 2 -4.37e-10 -5.09e-11 0.0364
5 FALSE 2.46 1 7.33e-11 -2.01e- 2 7.33e-11 -1.75e-10 0.0201
6 TRUE 2.46 6 -5.10e-10 -3.64e- 2 -5.10e-10 -7.93e-11 0.0364
7 FALSE 2.79 1 1.10e- 9 1.67e-10 1.10e- 9 1.45e- 9 0.00000000174
8 TRUE 2.79 6 1.32e- 9 -1.62e- 2 1.32e- 9 3.23e- 9 0.0162
9 FALSE 3.19 3 1.34e- 9 -9.74e- 3 1.34e- 9 1.51e- 9 0.00974
10 TRUE 3.19 4 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
11 FALSE 3.44 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
12 TRUE 3.44 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
13 FALSE 3.52 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
14 TRUE 3.52 0 1.56e- 9 -1.62e- 2 1.56e- 9 2.90e- 9 0.0162
15 FALSE 3.73 5 1.34e- 9 -1.69e- 5 1.34e- 9 2.11e- 9 0.0000169
16 TRUE 3.73 2 1.56e- 9 -2.60e- 2 1.56e- 9 2.66e- 9 0.0260
17 FALSE 4.05 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
18 TRUE 4.05 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
19 FALSE 5.45 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
20 TRUE 5.45 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
attr(,"effect")$`North America`$ale$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
attr(,"effect")$`North America`$ale$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
attr(,"effect")$`North America`$ale$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
attr(,"effect")$`North America`$ale$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
attr(,"effect")$`North America`$ale$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
attr(,"effect")$`North America`$ale$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
attr(,"effect")$`North America`$stats
attr(,"effect")$`North America`$stats$d1
# A tibble: 42 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg 0 0 0 0
2 aler_min 0 1 mpg 0 0 0 0
3 aler_max 0 1 mpg 0 0 0 0
4 naled 0 0 mpg 0 0 0 0
5 naler_min 0 1 mpg 0 0 0 0
6 naler_max 0 0 mpg 0 0 0 0
7 aled 0 1 vs 0 0 0 0
8 aler_min 0 1 vs 0 0 0 0
9 aler_max 0 1 vs 0 0 0 0
10 naled 0 0 vs 0 0 0 0
# i 32 more rows
attr(,"effect")$`North America`$stats$d2
# A tibble: 126 x 8
statistic estimate p.value term conf.low mean median conf.high
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 aled 0 1 mpg:vs 0 0 0 0
2 aler_min 0 1 mpg:vs 0 0 0 0
3 aler_max 0 1 mpg:vs 0 0 0 0
4 naled 0 0 mpg:vs 0 0 0 0
5 naler_min 0 1 mpg:vs 0 0 0 0
6 naler_max 0 0 mpg:vs 0 0 0 0
7 aled 0 1 mpg:am 0 0 0 0
8 aler_min 0 1 mpg:am 0 0 0 0
9 aler_max 0 1 mpg:am 0 0 0 0
10 naled 0 0 mpg:am 0 0 0 0
# i 116 more rows
attr(,"effect")$`North America`$boot_data
attr(,"effect")$`North America`$boot_data$d1
attr(,"effect")$`North America`$boot_data$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 0 1 0 0
2 0 14.4 0 7 0 0
3 0 15.2 0 7 0 0
4 0 16.4 0 7 0 0
5 0 18.7 0 7 0 0
6 0 19.8 0 7 0 0
7 0 21.4 0 7 0 0
8 0 22.9 0 7 0 0
9 0 30.1 0 7 0 0
10 0 33.9 0 7 0 0
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0 36 0 0
2 0 TRUE 0 28 0 0
3 1 FALSE 0 36 0 0
4 1 TRUE 0 28 0 0
5 2 FALSE 0 36 0 0
6 2 TRUE 0 28 0 0
attr(,"effect")$`North America`$boot_data$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.52e-10 38 1.52e-10 1.52e-10
2 0 TRUE -2.23e-10 26 -2.23e-10 -2.23e-10
3 1 FALSE -9.71e-10 38 -9.71e-10 -9.71e-10
4 1 TRUE 7.39e-10 26 7.39e-10 7.39e-10
5 2 FALSE -8.52e-10 38 -8.52e-10 -8.52e-10
6 2 TRUE -4.72e-11 26 -4.72e-11 -4.72e-11
attr(,"effect")$`North America`$boot_data$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.139 2 0.139 0.139
2 0 Cadillac Fleetwood 0.139 2 0.139 0.139
3 0 Merc 450SL -0.861 2 -0.861 -0.861
4 0 Merc 450SLC -0.861 2 -0.861 -0.861
5 0 Camaro Z28 0.139 2 0.139 0.139
6 0 Chrysler Imperial 0.139 2 0.139 0.139
7 0 Duster 360 0.139 2 0.139 0.139
8 0 Merc 450SE -0.861 2 -0.861 -0.861
9 0 Hornet Sportabout 0.139 2 0.139 0.139
10 0 AMC Javelin 0.139 2 0.139 0.139
# i 86 more rows
attr(,"effect")$`North America`$boot_data$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.254 30 0.254 0.254
2 0 four -0.283 24 -0.283 -0.283
3 0 five -0.0843 10 -0.0843 -0.0843
4 1 three 0.0526 30 0.0526 0.0526
5 1 four -0.232 24 -0.232 -0.232
6 1 five -0.133 10 -0.133 -0.133
7 2 three 0.0544 30 0.0544 0.0544
8 2 four -0.232 24 -0.232 -0.232
9 2 five 0.0679 10 0.0679 0.0679
attr(,"effect")$`North America`$boot_data$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.08e-7 14 1.08e-7 1.08e-7
2 0 2 -3.71e-8 19 -3.71e-8 -3.71e-8
3 0 3 -3.71e-8 9 -3.71e-8 -3.71e-8
4 0 4 -3.71e-8 16 -3.71e-8 -3.71e-8
5 0 8 -4.13e-8 6 -4.13e-8 -4.13e-8
6 1 1 1.08e-7 18 1.08e-7 1.08e-7
7 1 2 -7.21e-8 16 -7.21e-8 -7.21e-8
8 1 3 -7.21e-8 13 -7.21e-8 -7.21e-8
9 1 4 -7.21e-8 15 -7.21e-8 -7.21e-8
10 1 8 -8.38e-8 2 -8.38e-8 -8.38e-8
11 2 1 1.08e-7 15 1.08e-7 1.08e-7
12 2 2 1.40e-9 13 1.40e-9 1.40e-9
13 2 3 1.40e-9 13 1.40e-9 1.40e-9
14 2 4 1.40e-9 20 1.40e-9 1.40e-9
15 2 8 1.17e-9 3 1.17e-9 1.17e-9
attr(,"effect")$`North America`$boot_data$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 6.60e-10 1 6.60e-10 6.60e-10
2 0 1.94 -8.93e-12 7 -8.93e-12 -8.93e-12
3 0 2.46 -4.95e-11 7 -4.95e-11 -4.95e-11
4 0 2.79 -4.95e-11 7 -4.95e-11 -4.95e-11
5 0 3.19 -4.95e-11 7 -4.95e-11 -4.95e-11
6 0 3.44 -4.87e-11 7 -4.87e-11 -4.87e-11
7 0 3.52 -4.87e-11 7 -4.87e-11 -4.87e-11
8 0 3.73 -4.87e-11 7 -4.87e-11 -4.87e-11
9 0 4.05 -4.87e-11 7 -4.87e-11 -4.87e-11
10 0 5.45 -4.87e-11 7 -4.87e-11 -4.87e-11
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2
attr(,"effect")$`North America`$boot_data$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 7 0 0
4 0 16.4 FALSE 0 7 0 0
5 0 18.7 FALSE 0 3 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 4 0 0
8 0 22.9 FALSE 0 0 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 0 1 0 0
2 0 14.4 FALSE 0 7 0 0
3 0 15.2 FALSE 0 5 0 0
4 0 16.4 FALSE 0 5 0 0
5 0 18.7 FALSE 0 7 0 0
6 0 19.8 FALSE 0 5 0 0
7 0 21.4 FALSE 0 1 0 0
8 0 22.9 FALSE 0 5 0 0
9 0 30.1 FALSE 0 2 0 0
10 0 33.9 FALSE 0 0 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 0 0 0 0
2 0 14.4 Lincoln Continental 0 2 0 0
3 0 15.2 Lincoln Continental 0 0 0 0
4 0 16.4 Lincoln Continental 0 0 0 0
5 0 18.7 Lincoln Continental 0 0 0 0
6 0 19.8 Lincoln Continental 0 0 0 0
7 0 21.4 Lincoln Continental 0 0 0 0
8 0 22.9 Lincoln Continental 0 0 0 0
9 0 30.1 Lincoln Continental 0 0 0 0
10 0 33.9 Lincoln Continental 0 0 0 0
# i 950 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 0 1 0 0
2 0 14.4 three 0 7 0 0
3 0 15.2 three 0 5 0 0
4 0 16.4 three 0 5 0 0
5 0 18.7 three 0 5 0 0
6 0 19.8 three 0 3 0 0
7 0 21.4 three 0 1 0 0
8 0 22.9 three 0 3 0 0
9 0 30.1 three 0 0 0 0
10 0 33.9 three 0 0 0 0
# i 80 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 0 0 0 0
2 0 14.4 1 0 0 0 0
3 0 15.2 1 0 0 0 0
4 0 16.4 1 0 0 0 0
5 0 18.7 1 0 2 0 0
6 0 19.8 1 0 0 0 0
7 0 21.4 1 0 1 0 0
8 0 22.9 1 0 5 0 0
9 0 30.1 1 0 2 0 0
10 0 33.9 1 0 4 0 0
# i 140 more rows
attr(,"effect")$`North America`$boot_data$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 0 0 0 0
2 0 14.4 1.50 0 0 0 0
3 0 15.2 1.50 0 0 0 0
4 0 16.4 1.50 0 0 0 0
5 0 18.7 1.50 0 0 0 0
6 0 19.8 1.50 0 0 0 0
7 0 21.4 1.50 0 0 0 0
8 0 22.9 1.50 0 0 0 0
9 0 30.1 1.50 0 1 0 0
10 0 33.9 1.50 0 0 0 0
# i 290 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 0 24 0 0
2 0 TRUE FALSE 0 14 0 0
3 0 FALSE TRUE 0 12 0 0
4 0 TRUE TRUE 0 14 0 0
5 1 FALSE FALSE 0 24 0 0
6 1 TRUE FALSE 0 14 0 0
7 1 FALSE TRUE 0 12 0 0
8 1 TRUE TRUE 0 14 0 0
9 2 FALSE FALSE 0 24 0 0
10 2 TRUE FALSE 0 14 0 0
11 2 FALSE TRUE 0 12 0 0
12 2 TRUE TRUE 0 14 0 0
attr(,"effect")$`North America`$boot_data$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0 2 0 0
2 0 TRUE Lincoln Continental 0 0 0 0
3 0 FALSE Cadillac Fleetwood 0 2 0 0
4 0 TRUE Cadillac Fleetwood 0 0 0 0
5 0 FALSE Merc 450SL 0 2 0 0
6 0 TRUE Merc 450SL 0 0 0 0
7 0 FALSE Merc 450SLC 0 2 0 0
8 0 TRUE Merc 450SLC 0 0 0 0
9 0 FALSE Camaro Z28 0 2 0 0
10 0 TRUE Camaro Z28 0 0 0 0
# i 182 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0 24 0 0
2 0 TRUE three 0 6 0 0
3 0 FALSE four 0 4 0 0
4 0 TRUE four 0 20 0 0
5 0 FALSE five 0 8 0 0
6 0 TRUE five 0 2 0 0
7 1 FALSE three 0 24 0 0
8 1 TRUE three 0 6 0 0
9 1 FALSE four 0 4 0 0
10 1 TRUE four 0 20 0 0
11 1 FALSE five 0 8 0 0
12 1 TRUE five 0 2 0 0
13 2 FALSE three 0 24 0 0
14 2 TRUE three 0 6 0 0
15 2 FALSE four 0 4 0 0
16 2 TRUE four 0 20 0 0
17 2 FALSE five 0 8 0 0
18 2 TRUE five 0 2 0 0
attr(,"effect")$`North America`$boot_data$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0 0 0 0
2 0 TRUE 1 0 14 0 0
3 0 FALSE 2 0 10 0 0
4 0 TRUE 2 0 9 0 0
5 0 FALSE 3 0 8 0 0
6 0 TRUE 3 0 1 0 0
7 0 FALSE 4 0 13 0 0
8 0 TRUE 4 0 3 0 0
9 0 FALSE 8 0 5 0 0
10 0 TRUE 8 0 1 0 0
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0 0 0 0
2 0 TRUE 1.50 0 1 0 0
3 0 FALSE 1.94 0 0 0 0
4 0 TRUE 1.94 0 7 0 0
5 0 FALSE 2.46 0 2 0 0
6 0 TRUE 2.46 0 5 0 0
7 0 FALSE 2.79 0 4 0 0
8 0 TRUE 2.79 0 3 0 0
9 0 FALSE 3.19 0 4 0 0
10 0 TRUE 3.19 0 3 0 0
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0218 2 -0.0218 -0.0218
2 0 TRUE Lincoln Continental -0.0218 0 -1.51 -1.51
3 0 FALSE Cadillac Fleetwood -0.0218 2 -0.0218 -0.0218
4 0 TRUE Cadillac Fleetwood -0.0218 0 -1.51 -1.51
5 0 FALSE Merc 450SL -0.0218 2 -0.0218 -0.0218
6 0 TRUE Merc 450SL -0.0218 0 -1.51 -1.51
7 0 FALSE Merc 450SLC -0.0218 2 -0.0218 -0.0218
8 0 TRUE Merc 450SLC -0.0218 0 -1.51 -1.51
9 0 FALSE Camaro Z28 -0.0218 2 -0.0218 -0.0218
10 0 TRUE Camaro Z28 -0.0218 0 -1.51 -1.51
# i 182 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0.0729 30 0.205 0.205
2 0 TRUE three 0.0729 0 0.894 0.894
3 0 FALSE four 0.0729 8 0.205 0.205
4 0 TRUE four 0.643 16 1.46 1.46
5 0 FALSE five 0.0729 0 0.205 0.205
6 0 TRUE five 0.838 10 1.66 1.66
7 1 FALSE three 0.271 30 0.329 0.329
8 1 TRUE three 0.271 0 0.170 0.170
9 1 FALSE four 0.527 8 0.585 0.585
10 1 TRUE four 0.848 16 0.746 0.746
11 1 FALSE five 0.527 0 0.585 0.585
12 1 TRUE five 0.945 10 0.843 0.843
13 2 FALSE three 0.0732 30 -0.117 -0.117
14 2 TRUE three 0.0732 0 -0.646 -0.646
15 2 FALSE four 0.340 8 0.150 0.150
16 2 TRUE four 0.403 16 -0.317 -0.317
17 2 FALSE five 0.340 0 0.150 0.150
18 2 TRUE five 0.403 10 -0.317 -0.317
attr(,"effect")$`North America`$boot_data$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0000000460 6 0.00370 0.00370
2 0 TRUE 1 -0.0000000460 8 -0.123 -0.123
3 0 FALSE 2 -0.0000000460 12 -0.0828 -0.0828
4 0 TRUE 2 -0.000000439 7 -0.127 -0.127
5 0 FALSE 3 -0.0000000460 7 0.00369 0.00369
6 0 TRUE 3 -0.000000439 2 -0.210 -0.210
7 0 FALSE 4 -0.0000000460 11 -0.0000000460 -0.0000000460
8 0 TRUE 4 -0.000000446 5 -0.123 -0.123
9 0 FALSE 8 -0.0000000460 2 -0.0828 -0.0828
10 0 TRUE 8 -0.000000447 4 -0.210 -0.210
# i 20 more rows
attr(,"effect")$`North America`$boot_data$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 9.93e-11 0 1.17e- 5 1.17e- 5
2 0 TRUE 1.50 9.93e-11 1 -1.71e- 2 -1.71e- 2
3 0 FALSE 1.94 9.93e-11 0 -2.12e- 2 -2.12e- 2
4 0 TRUE 1.94 -5.71e-10 7 -3.83e- 2 -3.83e- 2
5 0 FALSE 2.46 9.93e-11 1 -2.12e- 2 -2.12e- 2
6 0 TRUE 2.46 -6.18e-10 6 -3.83e- 2 -3.83e- 2
7 0 FALSE 2.79 9.93e-11 1 9.93e-11 9.93e-11
8 0 TRUE 2.79 7.83e-10 6 -1.71e- 2 -1.71e- 2
9 0 FALSE 3.19 9.93e-11 3 -1.03e- 2 -1.03e- 2
10 0 TRUE 3.19 7.86e-10 4 -3.83e- 2 -3.83e- 2
# i 50 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.117 2 -0.117 -0.117
2 0 Cadillac Fleetwood three -0.117 2 -0.117 -0.117
3 0 Merc 450SL three -0.117 2 -0.117 -0.117
4 0 Merc 450SLC three -0.117 2 -0.117 -0.117
5 0 Camaro Z28 three -0.117 2 -0.117 -0.117
6 0 Chrysler Imperial three -0.117 2 0.339 0.339
7 0 Duster 360 three -0.117 2 0.317 0.317
8 0 Merc 450SE three -0.117 2 0.317 0.317
9 0 Hornet Sportabout three -0.117 2 0.434 0.434
10 0 AMC Javelin three -0.117 2 0.547 0.547
# i 278 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 0.471 0 0.471 0.471
2 0 Cadillac Fleetwood 1 0.471 0 0.471 0.471
3 0 Merc 450SL 1 0.471 0 0.471 0.471
4 0 Merc 450SLC 1 0.471 0 0.471 0.471
5 0 Camaro Z28 1 0.471 0 0.471 0.471
6 0 Chrysler Imperial 1 0.471 0 -1.53 -1.53
7 0 Duster 360 1 0.471 0 -5.55 -5.55
8 0 Merc 450SE 1 0.471 0 -6.69 -6.69
9 0 Hornet Sportabout 1 0.471 0 -6.97 -6.97
10 0 AMC Javelin 1 0.471 0 -7.07 -7.07
# i 470 more rows
attr(,"effect")$`North America`$boot_data$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.0603 0 0.0603 0.0603
2 0 Cadillac Fleetwood 1.50 0.0603 0 0.0603 0.0603
3 0 Merc 450SL 1.50 0.0603 0 0.0603 0.0603
4 0 Merc 450SLC 1.50 0.0603 0 0.0603 0.0603
5 0 Camaro Z28 1.50 0.0603 0 0.0602 0.0602
6 0 Chrysler Imperial 1.50 0.0603 0 0.00250 0.00250
7 0 Duster 360 1.50 0.0603 0 -0.252 -0.252
8 0 Merc 450SE 1.50 0.0603 0 -0.318 -0.318
9 0 Hornet Sportabout 1.50 0.0603 0 -0.334 -0.334
10 0 AMC Javelin 1.50 0.0603 0 -0.334 -0.334
# i 950 more rows
attr(,"effect")$`North America`$boot_data$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 -0.0306 6 -0.108 -0.108
2 0 four 1 -0.0306 8 -0.157 -0.157
3 0 five 1 -0.0306 0 -0.160 -0.160
4 0 three 2 -0.0306 8 -0.213 -0.213
5 0 four 2 -0.165 7 -0.347 -0.347
6 0 five 2 -0.165 4 -0.242 -0.242
7 0 three 3 -0.0306 7 -0.157 -0.157
8 0 four 3 -0.195 2 -0.325 -0.325
9 0 five 3 -0.198 0 -0.380 -0.380
10 0 three 4 -0.0306 8 -0.213 -0.213
# i 35 more rows
attr(,"effect")$`North America`$boot_data$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.0342 0 -0.0903 -0.0903
2 0 four 1.50 -0.0342 0 -0.0997 -0.0997
3 0 five 1.50 -0.0342 1 -0.106 -0.106
4 0 three 1.94 -0.0342 0 -0.119 -0.119
5 0 four 1.94 -0.0506 6 -0.157 -0.157
6 0 five 1.94 -0.0506 1 -0.157 -0.157
7 0 three 2.46 -0.0342 1 -0.141 -0.141
8 0 four 2.46 -0.0578 4 -0.164 -0.164
9 0 five 2.46 -0.0578 2 -0.164 -0.164
10 0 three 2.79 -0.0342 1 -0.141 -0.141
# i 80 more rows
attr(,"effect")$`North America`$boot_data$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.0000000171 0 -0.0332 -0.0332
2 0 2 1.50 0.0000000171 1 -0.00425 -0.00425
3 0 3 1.50 0.0000000171 0 -0.0375 -0.0375
4 0 4 1.50 0.0000000171 0 -0.0281 -0.0281
5 0 8 1.50 0.0000000171 0 -0.0381 -0.0381
6 0 1 1.94 0.0000000171 4 -0.0415 -0.0415
7 0 2 1.94 0.000000145 3 -0.00831 -0.00831
8 0 3 1.94 0.000000145 0 -0.0415 -0.0415
9 0 4 1.94 0.000000145 0 -0.0316 -0.0316
10 0 8 1.94 0.000000145 0 -0.0415 -0.0415
# i 140 more rows
attr(,"params")
attr(,"params")$max_d
[1] 2
attr(,"params")$ordered_x_cols
attr(,"params")$ordered_x_cols$d1
[1] "mpg" "vs" "am" "model" "gear" "carb" "wt"
attr(,"params")$ordered_x_cols$d2
[1] "mpg:vs" "mpg:am" "mpg:model" "mpg:gear" "mpg:carb"
[6] "mpg:wt" "vs:am" "vs:model" "vs:gear" "vs:carb"
[11] "vs:wt" "am:model" "am:gear" "am:carb" "am:wt"
[16] "model:gear" "model:carb" "model:wt" "gear:carb" "gear:wt"
[21] "carb:wt"
attr(,"params")$requested_x_cols
attr(,"params")$requested_x_cols$d1
[1] "mpg" "vs" "am" "model" "gear" "carb" "wt"
attr(,"params")$requested_x_cols$d2
[1] "mpg:vs" "mpg:am" "mpg:model" "mpg:gear" "mpg:carb"
[6] "mpg:wt" "vs:am" "vs:model" "vs:gear" "vs:carb"
[11] "vs:wt" "am:model" "am:gear" "am:carb" "am:wt"
[16] "model:gear" "model:carb" "model:wt" "gear:carb" "gear:wt"
[21] "carb:wt"
attr(,"params")$y_cats
[1] "Asia" "Europe" "North America"
attr(,"params")$y_summary
continent Asia Europe North America
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
1% 1.069668e-33 2.992722e-30 1.069668e-33 2.347822e-34
2.5% 6.673430e-30 4.423735e-28 1.517465e-32 6.673430e-30
5% 3.090197e-29 3.706601e-27 1.024412e-30 3.090197e-29
10% 7.497546e-28 7.833412e-24 7.497546e-28 8.934872e-29
20% 4.002630e-25 6.716097e-21 4.002630e-25 4.655065e-26
25% 3.199266e-24 8.785222e-20 8.693125e-25 3.199266e-24
30% 6.827727e-23 2.256763e-18 6.827727e-23 2.889855e-23
40% 7.271081e-18 8.144755e-16 5.332080e-18 7.271081e-18
aler_lo_lo 1.197313e-14 1.197313e-14 1.305088e-14 2.637676e-14
aler_lo 1.197341e-14 1.197341e-14 1.305116e-14 2.637704e-14
50% 1.305186e-14 1.197411e-14 1.305186e-14 2.637774e-14
mean 3.333333e-01 3.333333e-01 3.333333e-01 3.333333e-01
aler_hi 2.637802e-14 1.197440e-14 1.305215e-14 2.637802e-14
aler_hi_hi 2.637866e-14 1.197504e-14 1.305278e-14 2.637866e-14
60% 1.134760e-08 9.938550e-13 9.999865e-01 1.134760e-08
70% 1.000000e+00 5.030736e-08 1.000000e+00 1.000000e+00
75% 1.000000e+00 4.755457e-06 1.000000e+00 1.000000e+00
80% 1.000000e+00 1.373114e-05 1.000000e+00 1.000000e+00
90% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
95% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
97.5% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
99% 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
max 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
attr(,"params")$model
attr(,"params")$model$class
[1] "multinom" "nnet"
attr(,"params")$model$call
[1] "nnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)"
attr(,"params")$model$print
[1] "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)\n\nCoefficients:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope -21.86205 -2.222771 -6.526356\nNorth America -12.29302 -2.006450 14.817906\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope -3.5495185 -42.28844 -3.766810\nNorth America -0.4119232 -22.35666 7.689393\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope -7.046867 5.545836 42.144872 43.426334\nNorth America 20.851301 -3.241914 2.522922 2.870462\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope -23.11127 -27.5878163 -2.495884\nNorth America 29.30682 -0.1513286 7.771946\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope -5.036042 -1.780289 14.561696\nNorth America 12.103127 -2.891703 -1.631007\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 6.999218 -33.61112 -35.88135\nNorth America -7.857123 -3.27005 -10.72975\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 14.2684900 14.2452958 11.590028 10.4496565\nNorth America -0.5204999 -0.6708756 1.228568 0.8928993\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 39.49499 34.5616 36.09627\nNorth America -38.59611 -31.8537 -34.17067\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope -3.057543 15.344405 -34.531039\nNorth America 4.399831 -4.102117 -8.804816\n modelToyota Corona modelValiant modelVolvo 142E wt\nEurope -44.72605 -2.073514 34.932615 4.534617\nNorth America -52.77315 5.537827 2.626442 3.625818\n amTRUE gear.L gear.Q carb\nEurope -34.05607 40.7995829 -5.443825 10.524876\nNorth America -24.81811 0.6220113 39.516400 5.894396\n\nResidual Deviance: 0.0001161393 \nAIC: 136.0001 "
attr(,"params")$model$summary
[1] "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)\n\nCoefficients:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope -21.86205 -2.222771 -6.526356\nNorth America -12.29302 -2.006450 14.817906\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope -3.5495185 -42.28844 -3.766810\nNorth America -0.4119232 -22.35666 7.689393\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope -7.046867 5.545836 42.144872 43.426334\nNorth America 20.851301 -3.241914 2.522922 2.870462\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope -23.11127 -27.5878163 -2.495884\nNorth America 29.30682 -0.1513286 7.771946\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope -5.036042 -1.780289 14.561696\nNorth America 12.103127 -2.891703 -1.631007\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 6.999218 -33.61112 -35.88135\nNorth America -7.857123 -3.27005 -10.72975\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 14.2684900 14.2452958 11.590028 10.4496565\nNorth America -0.5204999 -0.6708756 1.228568 0.8928993\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 39.49499 34.5616 36.09627\nNorth America -38.59611 -31.8537 -34.17067\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope -3.057543 15.344405 -34.531039\nNorth America 4.399831 -4.102117 -8.804816\n modelToyota Corona modelValiant modelVolvo 142E wt\nEurope -44.72605 -2.073514 34.932615 4.534617\nNorth America -52.77315 5.537827 2.626442 3.625818\n amTRUE gear.L gear.Q carb\nEurope -34.05607 40.7995829 -5.443825 10.524876\nNorth America -24.81811 0.6220113 39.516400 5.894396\n\nStd. Errors:\n (Intercept) modelCadillac Fleetwood modelCamaro Z28\nEurope 35852.83 64.65729 NaN\nNorth America 12968.78 64.70730 2.870067e-08\n modelChrysler Imperial modelDatsun 710 modelDodge Challenger\nEurope 477.3874 1.590757e-08 2.858243e-08\nNorth America 477.3874 2.200012e-18 6.428719e+00\n modelDuster 360 modelFerrari Dino modelFiat 128 modelFiat X1-9\nEurope 5.486580e-10 2660.898 1.805399e+04 1.525317e+04\nNorth America 7.249665e-11 2660.898 1.192455e-12 4.867466e-13\n modelFord Pantera L modelHonda Civic modelHornet 4 Drive\nEurope 0.09603878 5.871595e-07 3.863891e-10\nNorth America 0.09624948 9.331882e-08 7.163294e+03\n modelHornet Sportabout modelLincoln Continental modelLotus Europa\nEurope 4.655689e-09 285.0759 32194.07\nNorth America 5.351209e-02 285.1436 20099.45\n modelMaserati Bora modelMazda RX4 modelMazda RX4 Wag\nEurope 0.01739745 644.72277405 1.000913e+02\nNorth America 0.01739745 0.04282998 3.394594e-05\n modelMerc 230 modelMerc 240D modelMerc 280 modelMerc 280C\nEurope 1.329222e+02 1.088958e+02 4.023889e-07 2.701645e-06\nNorth America 4.475756e-08 3.800080e-08 2.855911e-10 1.280090e-09\n modelMerc 450SE modelMerc 450SL modelMerc 450SLC\nEurope 92448.24826 22918.50 1179.3388\nNorth America 12.05893 21677.09 776.0876\n modelPontiac Firebird modelPorsche 914-2 modelToyota Corolla\nEurope 1.504060e-06 29024.38 9.393802e-15\nNorth America 6.422677e+01 62881.87 2.008891e-13\n modelToyota Corona modelValiant modelVolvo 142E wt amTRUE\nEurope 2.042457e-17 7.332842e-09 5.751337e+04 26852.73 81773.79\nNorth America 1.689525e+01 3.329289e+04 5.749185e-12 97682.05 57698.40\n gear.L gear.Q carb\nEurope 120502.95 45794.583 77388.60\nNorth America 84695.32 5294.488 34719.66\n\nResidual Deviance: 0.0001161393 \nAIC: 136.0001 "
attr(,"params")$data
attr(,"params")$data$data_sample
# A tibble: 64 x 8
mpg vs continent am model gear carb wt
<dbl> <lgl> <fct> <lgl> <chr> <ord> <int> <dbl>
1 21 FALSE Asia TRUE Mazda RX4 four 4 2.62
2 21 FALSE Asia TRUE Mazda RX4 Wag four 4 2.88
3 22.8 TRUE Asia TRUE Datsun 710 four 1 2.32
4 21.4 TRUE North America FALSE Hornet 4 Drive three 1 3.22
5 18.7 FALSE North America FALSE Hornet Sportabout three 2 3.44
6 18.1 TRUE North America FALSE Valiant three 1 3.46
7 14.3 FALSE North America FALSE Duster 360 three 4 3.57
8 24.4 TRUE Europe FALSE Merc 240D four 2 3.19
9 22.8 TRUE Europe FALSE Merc 230 four 2 3.15
10 19.2 TRUE Europe FALSE Merc 280 four 4 3.44
# i 54 more rows
attr(,"params")$data$y_vals_sample
Asia Europe North America
1 1.000000e+00 3.101887e-14 6.537610e-18
2 1.000000e+00 1.018274e-14 9.489785e-21
3 1.000000e+00 2.627972e-32 2.366458e-34
4 3.355506e-13 2.265633e-26 1.000000e+00
5 5.377066e-18 2.956660e-27 1.000000e+00
6 1.288929e-12 4.032610e-25 1.000000e+00
7 4.043404e-27 7.439102e-28 1.000000e+00
8 8.984341e-15 1.000000e+00 2.843402e-24
9 1.052416e-14 1.000000e+00 3.348575e-24
10 2.968411e-23 1.000000e+00 2.047714e-26
11 9.284979e-23 1.000000e+00 4.578751e-26
12 1.284023e-16 1.000000e+00 8.157836e-18
13 8.331004e-14 1.000000e+00 1.307688e-12
14 1.431293e-14 1.000000e+00 2.654720e-14
15 7.731999e-20 3.603019e-15 1.000000e+00
16 9.971464e-20 1.592098e-14 1.000000e+00
17 1.112253e-20 2.115769e-16 1.000000e+00
18 1.405315e-05 9.999859e-01 1.373965e-28
19 1.000000e+00 9.694352e-23 2.934333e-23
20 1.000000e+00 6.815207e-30 3.132362e-29
21 1.000000e+00 1.028218e-33 9.973236e-16
22 3.322245e-16 9.342366e-25 1.000000e+00
23 9.879611e-13 8.170982e-20 1.000000e+00
24 6.336585e-25 6.674374e-25 1.000000e+00
25 2.743479e-15 6.845408e-23 1.000000e+00
26 1.297545e-05 9.999870e-01 6.870224e-29
27 5.019518e-08 9.999999e-01 3.082157e-10
28 1.885279e-06 9.999981e-01 1.410710e-08
29 9.136296e-20 5.361045e-18 1.000000e+00
30 2.703016e-23 1.000000e+00 6.687763e-14
31 1.211644e-34 1.000000e+00 7.112017e-21
32 3.688923e-08 1.000000e+00 1.189251e-27
33 1.000000e+00 3.408497e-14 7.049392e-18
34 1.000000e+00 2.572858e-19 2.489552e-23
35 1.000000e+00 2.558207e-32 2.316091e-34
36 3.298994e-13 2.275299e-26 1.000000e+00
37 1.337882e-20 3.110842e-25 1.000000e+00
38 1.389107e-12 3.957660e-25 1.000000e+00
39 3.647166e-27 7.633916e-28 1.000000e+00
40 7.899779e-15 1.000000e+00 2.771028e-24
41 1.005165e-14 1.000000e+00 3.317888e-24
42 7.658342e-28 1.000000e+00 1.980485e-28
43 1.064524e-22 1.000000e+00 4.705941e-26
44 5.328465e-12 1.000000e+00 8.550926e-16
45 2.496435e-18 1.000000e+00 1.303274e-14
46 1.342406e-14 1.000000e+00 2.620827e-14
47 2.933308e-17 3.474335e-17 1.000000e+00
48 3.255127e-17 1.594157e-16 1.000000e+00
49 3.066813e-23 2.169517e-14 1.000000e+00
50 1.345603e-05 9.999865e-01 1.362061e-28
51 1.000000e+00 1.041859e-22 3.108341e-23
52 1.000000e+00 6.680493e-30 3.082756e-29
53 1.000000e+00 1.094012e-33 1.048032e-15
54 2.973320e-16 9.605844e-25 1.000000e+00
55 1.061403e-12 8.025443e-20 1.000000e+00
56 6.074529e-25 6.745405e-25 1.000000e+00
57 3.045398e-15 6.668596e-23 1.000000e+00
58 1.351647e-05 9.999865e-01 6.926697e-29
59 5.131701e-08 9.999999e-01 3.095840e-10
60 2.015459e-06 9.999980e-01 1.429715e-08
61 9.386677e-20 5.324839e-18 1.000000e+00
62 2.463284e-23 1.000000e+00 6.564435e-14
63 4.750282e-30 1.000000e+00 7.370275e-19
64 9.953285e-13 1.000000e+00 1.160579e-29
attr(,"params")$data$nrow
[1] 64
attr(,"params")$y_col
[1] "continent"
attr(,"params")$parallel
[1] 0
attr(,"params")$model_packages
NULL
attr(,"params")$output_stats
[1] TRUE
attr(,"params")$output_boot_data
[1] TRUE
attr(,"params")$pred_fun
[1] "function(object, newdata, type = pred_type) {\n stats::predict(object = object, newdata = newdata, type = type)\n }"
attr(,"params")$pred_type
[1] "probs"
attr(,"params")$p_values
<ale::ALEpDist>
@ rand_stats :List of 3
.. $ Asia : tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. $ Europe : tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. $ North America: tibble [100 x 6] (S3: tbl_df/tbl/data.frame)
.. ..$ aled : num [1:100] 4.92e-20 2.07e-22 1.29e-21 7.03e-23 4.80e-22 ...
.. ..$ aler_min : num [1:100] -2.26e-19 -6.32e-22 -4.70e-21 -3.06e-22 -1.53e-21 ...
.. ..$ aler_max : num [1:100] 2.66e-19 1.04e-21 4.96e-21 4.08e-22 2.85e-21 ...
.. ..$ naled : num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_min: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ naler_max: num [1:100] 0 0 0 0 0 0 0 0 0 0 ...
@ residual_distribution: 'univariateML' Named num [1:2] -2.04e-23 1.50e-17
.. - attr(*, "logLik")= num 2371
.. - attr(*, "call")= language f(x = x, na.rm = na.rm)
.. - attr(*, "n")= int 64
.. - attr(*, "model")= chr "Laplace"
.. - attr(*, "density")= chr "extraDistr::dlaplace"
.. - attr(*, "support")= num [1:2] -Inf Inf
.. - attr(*, "names")= chr [1:2] "mu" "sigma"
.. - attr(*, "default")= num [1:2] 0 1
.. - attr(*, "continuous")= logi TRUE
@ residuals : NULL
@ params :List of 11
.. $ model :List of 4
.. ..$ class : chr [1:2] "multinom" "nnet"
.. ..$ call : chr "nnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALSE)"
.. ..$ print : chr "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALS"| __truncated__
.. ..$ summary: chr "Call:\nnnet::multinom(formula = continent ~ model + wt + am + gear + \n carb, data = test_cars, trace = FALS"| __truncated__
.. $ y_col : chr "continent"
.. $ rand_it : NULL
.. $ parallel : num 0
.. $ model_packages : NULL
.. $ random_model_call_string : NULL
.. $ random_model_call_string_vars: chr(0)
.. $ positive : logi TRUE
.. $ seed : num 0
.. $ rand_it_ok : int 100
.. $ exactness : chr "surrogate"
attr(,"params")$aler_alpha
[1] 0.01 0.05
attr(,"params")$max_num_bins
[1] 10
attr(,"params")$boot_it
[1] 2
attr(,"params")$boot_alpha
[1] 0.05
attr(,"params")$boot_centre
[1] "mean"
attr(,"params")$seed
[1] 0
attr(,"params")$y_type
[1] "categorical"
attr(,"params")$sample_size
[1] 500
Code
get(cat_cars_ale)
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
$Asia$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
$Asia$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
$Asia$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
$Europe$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
$Europe$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
$Europe$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 28 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.71e-11 7.00e-10
$`North America`$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
$`North America`$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
$`North America`$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
$`North America`$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.47e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.35e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.28e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 Lincoln Contine~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 310 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 three 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 40 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 90 more rows
$`North America`$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE FALSE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE TRUE 12 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE TRUE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE Lincoln Continent~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE Cadillac Fleetwood 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE Cadillac Fleetwood 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE Merc 450SL 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE Merc 450SL 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE Merc 450SLC 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE Merc 450SLC 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE Camaro Z28 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE Camaro Z28 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 54 more rows
$`North America`$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE three 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE four 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE four 20 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE five 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE five 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 2 10 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 2 9 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 3 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 3 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 4 13 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 4 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 8 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 8 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 1.94 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 1.94 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 2.46 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 2.46 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 2.79 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 2.79 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 3.19 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 3.19 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 FALSE 3.44 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 TRUE 3.44 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 FALSE 3.52 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 TRUE 3.52 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 FALSE 3.73 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 TRUE 3.73 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 FALSE 4.05 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 TRUE 4.05 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 FALSE 5.45 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 TRUE 5.45 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
2 TRUE Lincoln Continental 0 -0.0218 -1.43 -0.0218 0.0596 1.32
3 FALSE Cadillac Fleetwood 2 -0.0218 -0.0218 -0.0218 -0.0218 -0.0218
4 TRUE Cadillac Fleetwood 0 -0.0218 -1.43 -0.0218 0.0596 1.32
5 FALSE Merc 450SL 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
6 TRUE Merc 450SL 0 -0.0226 -1.43 -0.0226 0.0596 1.31
7 FALSE Merc 450SLC 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
8 TRUE Merc 450SLC 0 -0.0226 -1.43 -0.0226 0.0596 1.31
9 FALSE Camaro Z28 2 -0.0226 -0.0241 -0.0226 -0.0218 -0.0218
10 TRUE Camaro Z28 0 -0.0226 -1.43 -0.0226 0.0596 1.31
# i 54 more rows
$`North America`$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 0.139 -0.101 0.139 0.205 0.323
2 TRUE three 0 0.139 -0.605 0.139 0.170 0.858
3 FALSE four 8 0.313 0.153 0.313 0.205 0.566
4 TRUE four 16 0.631 -0.263 0.631 0.746 1.43
5 FALSE five 0 0.313 0.153 0.313 0.205 0.566
6 TRUE five 10 0.728 -0.259 0.728 0.843 1.62
$`North America`$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0000000460 -0.00351 -4.60e-8 2.63e-8 3.51e-3
2 TRUE 1 8 -0.0000000460 -0.117 -4.60e-8 2.47e-7 1.17e-1
3 FALSE 2 12 -0.000000106 -0.0786 -1.06e-7 2.31e-8 7.86e-2
4 TRUE 2 7 -0.000000373 -0.121 -3.73e-7 -5.45e-8 1.21e-1
5 FALSE 3 7 -0.000000106 -0.00351 -1.06e-7 2.19e-8 3.50e-3
6 TRUE 3 2 -0.000000373 -0.199 -3.73e-7 1.90e-8 1.99e-1
7 FALSE 4 11 -0.000000109 -0.000000220 -1.09e-7 -5.33e-8 -4.64e-8
8 TRUE 4 5 -0.000000380 -0.117 -3.80e-7 1.25e-8 1.17e-1
9 FALSE 8 2 -0.000000110 -0.0786 -1.10e-7 1.95e-8 7.86e-2
10 TRUE 8 4 -0.000000380 -0.199 -3.80e-7 1.30e-8 1.99e-1
$`North America`$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 9.93e-11 -1.11e- 5 9.93e-11 4.34e-10 0.0000111
2 TRUE 1.50 1 9.93e-11 -1.62e- 2 9.93e-11 5.51e-10 0.0162
3 FALSE 1.94 0 9.85e-11 -2.01e- 2 9.85e-11 -1.47e-10 0.0201
4 TRUE 1.94 7 -4.37e-10 -3.64e- 2 -4.37e-10 -5.09e-11 0.0364
5 FALSE 2.46 1 7.33e-11 -2.01e- 2 7.33e-11 -1.75e-10 0.0201
6 TRUE 2.46 6 -5.10e-10 -3.64e- 2 -5.10e-10 -7.92e-11 0.0364
7 FALSE 2.79 1 1.10e- 9 1.67e-10 1.10e- 9 1.45e- 9 0.00000000174
8 TRUE 2.79 6 1.32e- 9 -1.62e- 2 1.32e- 9 3.24e- 9 0.0162
9 FALSE 3.19 3 1.34e- 9 -9.74e- 3 1.34e- 9 1.51e- 9 0.00974
10 TRUE 3.19 4 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
11 FALSE 3.44 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
12 TRUE 3.44 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
13 FALSE 3.52 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
14 TRUE 3.52 0 1.56e- 9 -1.62e- 2 1.56e- 9 2.90e- 9 0.0162
15 FALSE 3.73 5 1.34e- 9 -1.69e- 5 1.34e- 9 2.11e- 9 0.0000169
16 TRUE 3.73 2 1.56e- 9 -2.60e- 2 1.56e- 9 2.66e- 9 0.0260
17 FALSE 4.05 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
18 TRUE 4.05 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
19 FALSE 5.45 7 1.34e- 9 -2.01e- 2 1.34e- 9 1.51e- 9 0.0201
20 TRUE 5.45 0 1.56e- 9 -3.64e- 2 1.56e- 9 2.66e- 9 0.0364
$`North America`$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
$`North America`$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
$`North America`$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
$`North America`$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
$`North America`$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
$`North America`$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
Code
get(cat_cars_ale, what = "boot_data")
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 1.20e-14 7 1.20e-14 1.20e-14
4 0 16.4 1.20e-14 7 1.20e-14 1.20e-14
5 0 18.7 1.20e-14 7 1.20e-14 1.20e-14
6 0 19.8 1.20e-14 7 1.20e-14 1.20e-14
7 0 21.4 1.20e-14 7 1.20e-14 1.20e-14
8 0 22.9 1.20e-14 7 1.20e-14 1.20e-14
9 0 30.1 1.20e-14 7 1.20e-14 1.20e-14
10 0 33.9 1.20e-14 7 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.20e-14 36 1.20e-14 1.20e-14
2 0 TRUE 1.20e-14 28 1.20e-14 1.20e-14
3 1 FALSE 1.20e-14 36 1.20e-14 1.20e-14
4 1 TRUE 1.20e-14 28 1.20e-14 1.20e-14
5 2 FALSE 1.20e-14 36 1.20e-14 1.20e-14
6 2 TRUE 1.20e-14 28 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE -0.0432 38 -0.0432 -0.0432
2 0 TRUE 0.0632 26 0.0632 0.0632
3 1 FALSE 0.0321 38 0.0321 0.0321
4 1 TRUE -0.0101 26 -0.0101 -0.0101
5 2 FALSE 0.0297 38 0.0297 0.0297
6 2 TRUE -0.00670 26 -0.00670 -0.00670
$Asia$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental -0.305 2 -0.305 -0.305
2 0 Cadillac Fleetwood -0.305 2 -0.305 -0.305
3 0 Merc 450SL -0.305 2 -0.305 -0.305
4 0 Merc 450SLC -0.305 2 -0.305 -0.305
5 0 Camaro Z28 -0.305 2 -0.305 -0.305
6 0 Chrysler Imperial -0.305 2 -0.305 -0.305
7 0 Duster 360 -0.305 2 -0.305 -0.305
8 0 Merc 450SE -0.305 2 -0.305 -0.305
9 0 Hornet Sportabout -0.305 2 -0.305 -0.305
10 0 AMC Javelin -0.305 2 -0.305 -0.305
# i 86 more rows
$Asia$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.118 30 0.118 0.118
2 0 four 0.0718 24 0.0718 0.0718
3 0 five -0.526 10 -0.526 -0.526
4 1 three 0.0530 30 0.0530 0.0530
5 1 four -0.162 24 -0.162 -0.162
6 1 five -0.460 10 -0.460 -0.460
7 2 three 0.184 30 0.184 0.184
8 2 four 0.0960 24 0.0960 0.0960
9 2 five -0.00410 10 -0.00410 -0.00410
$Asia$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 0.00549 14 0.00549 0.00549
2 0 2 -0.00191 19 -0.00191 -0.00191
3 0 3 -0.00191 9 -0.00191 -0.00191
4 0 4 -0.00191 16 -0.00191 -0.00191
5 0 8 -0.00191 6 -0.00191 -0.00191
6 1 1 0.00549 18 0.00549 0.00549
7 1 2 -0.00377 16 -0.00377 -0.00377
8 1 3 -0.00377 13 -0.00377 -0.00377
9 1 4 -0.00377 15 -0.00377 -0.00377
10 1 8 -0.00377 2 -0.00377 -0.00377
11 2 1 0.00549 15 0.00549 0.00549
12 2 2 -0.0000232 13 -0.0000232 -0.0000232
13 2 3 -0.0000232 13 -0.0000232 -0.0000232
14 2 4 -0.0000232 20 -0.0000232 -0.0000232
15 2 8 -0.0000232 3 -0.0000232 -0.0000232
$Asia$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 0.0000319 1 0.0000319 0.0000319
2 0 1.94 0.00000854 7 0.00000854 0.00000854
3 0 2.46 -0.00000364 7 -0.00000364 -0.00000364
4 0 2.79 -0.00000366 7 -0.00000366 -0.00000366
5 0 3.19 -0.00000366 7 -0.00000366 -0.00000366
6 0 3.44 -0.00000366 7 -0.00000366 -0.00000366
7 0 3.52 -0.00000366 7 -0.00000366 -0.00000366
8 0 3.73 -0.00000366 7 -0.00000366 -0.00000366
9 0 4.05 -0.00000366 7 -0.00000366 -0.00000366
10 0 5.45 -0.00000366 7 -0.00000366 -0.00000366
# i 20 more rows
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 FALSE 1.20e-14 7 1.20e-14 1.20e-14
4 0 16.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
5 0 18.7 FALSE 1.20e-14 3 1.20e-14 1.20e-14
6 0 19.8 FALSE 1.20e-14 5 1.20e-14 1.20e-14
7 0 21.4 FALSE 1.20e-14 4 1.20e-14 1.20e-14
8 0 22.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 FALSE 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 FALSE 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 FALSE 1.20e-14 5 1.20e-14 1.20e-14
4 0 16.4 FALSE 1.20e-14 5 1.20e-14 1.20e-14
5 0 18.7 FALSE 1.20e-14 7 1.20e-14 1.20e-14
6 0 19.8 FALSE 1.20e-14 5 1.20e-14 1.20e-14
7 0 21.4 FALSE 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 FALSE 1.20e-14 5 1.20e-14 1.20e-14
9 0 30.1 FALSE 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 FALSE 1.20e-14 0 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 Lincoln Continental 1.20e-14 2 1.20e-14 1.20e-14
3 0 15.2 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
6 0 19.8 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
8 0 22.9 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
10 0 33.9 Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
# i 950 more rows
$Asia$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 1.20e-14 1 1.20e-14 1.20e-14
2 0 14.4 three 1.20e-14 7 1.20e-14 1.20e-14
3 0 15.2 three 1.20e-14 5 1.20e-14 1.20e-14
4 0 16.4 three 1.20e-14 5 1.20e-14 1.20e-14
5 0 18.7 three 1.20e-14 5 1.20e-14 1.20e-14
6 0 19.8 three 1.20e-14 3 1.20e-14 1.20e-14
7 0 21.4 three 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 three 1.20e-14 3 1.20e-14 1.20e-14
9 0 30.1 three 1.20e-14 0 1.20e-14 1.20e-14
10 0 33.9 three 1.20e-14 0 1.20e-14 1.20e-14
# i 80 more rows
$Asia$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 1 1.20e-14 0 1.20e-14 1.20e-14
3 0 15.2 1 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 1 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 1 1.20e-14 2 1.20e-14 1.20e-14
6 0 19.8 1 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 1 1.20e-14 1 1.20e-14 1.20e-14
8 0 22.9 1 1.20e-14 5 1.20e-14 1.20e-14
9 0 30.1 1 1.20e-14 2 1.20e-14 1.20e-14
10 0 33.9 1 1.20e-14 4 1.20e-14 1.20e-14
# i 140 more rows
$Asia$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
2 0 14.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
3 0 15.2 1.50 1.20e-14 0 1.20e-14 1.20e-14
4 0 16.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
5 0 18.7 1.50 1.20e-14 0 1.20e-14 1.20e-14
6 0 19.8 1.50 1.20e-14 0 1.20e-14 1.20e-14
7 0 21.4 1.50 1.20e-14 0 1.20e-14 1.20e-14
8 0 22.9 1.50 1.20e-14 0 1.20e-14 1.20e-14
9 0 30.1 1.50 1.20e-14 1 1.20e-14 1.20e-14
10 0 33.9 1.50 1.20e-14 0 1.20e-14 1.20e-14
# i 290 more rows
$Asia$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
2 0 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
3 0 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
4 0 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
5 1 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
6 1 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
7 1 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
8 1 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
9 2 FALSE FALSE 1.20e-14 24 1.20e-14 1.20e-14
10 2 TRUE FALSE 1.20e-14 14 1.20e-14 1.20e-14
11 2 FALSE TRUE 1.20e-14 12 1.20e-14 1.20e-14
12 2 TRUE TRUE 1.20e-14 14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 1.20e-14 2 1.20e-14 1.20e-14
2 0 TRUE Lincoln Continental 1.20e-14 0 1.20e-14 1.20e-14
3 0 FALSE Cadillac Fleetwood 1.20e-14 2 1.20e-14 1.20e-14
4 0 TRUE Cadillac Fleetwood 1.20e-14 0 1.20e-14 1.20e-14
5 0 FALSE Merc 450SL 1.20e-14 2 1.20e-14 1.20e-14
6 0 TRUE Merc 450SL 1.20e-14 0 1.20e-14 1.20e-14
7 0 FALSE Merc 450SLC 1.20e-14 2 1.20e-14 1.20e-14
8 0 TRUE Merc 450SLC 1.20e-14 0 1.20e-14 1.20e-14
9 0 FALSE Camaro Z28 1.20e-14 2 1.20e-14 1.20e-14
10 0 TRUE Camaro Z28 1.20e-14 0 1.20e-14 1.20e-14
# i 182 more rows
$Asia$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
2 0 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
3 0 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
4 0 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
5 0 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
6 0 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
7 1 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
8 1 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
9 1 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
10 1 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
11 1 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
12 1 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
13 2 FALSE three 1.20e-14 24 1.20e-14 1.20e-14
14 2 TRUE three 1.20e-14 6 1.20e-14 1.20e-14
15 2 FALSE four 1.20e-14 4 1.20e-14 1.20e-14
16 2 TRUE four 1.20e-14 20 1.20e-14 1.20e-14
17 2 FALSE five 1.20e-14 8 1.20e-14 1.20e-14
18 2 TRUE five 1.20e-14 2 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 1.20e-14 0 1.20e-14 1.20e-14
2 0 TRUE 1 1.20e-14 14 1.20e-14 1.20e-14
3 0 FALSE 2 1.20e-14 10 1.20e-14 1.20e-14
4 0 TRUE 2 1.20e-14 9 1.20e-14 1.20e-14
5 0 FALSE 3 1.20e-14 8 1.20e-14 1.20e-14
6 0 TRUE 3 1.20e-14 1 1.20e-14 1.20e-14
7 0 FALSE 4 1.20e-14 13 1.20e-14 1.20e-14
8 0 TRUE 4 1.20e-14 3 1.20e-14 1.20e-14
9 0 FALSE 8 1.20e-14 5 1.20e-14 1.20e-14
10 0 TRUE 8 1.20e-14 1 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 1.20e-14 0 1.20e-14 1.20e-14
2 0 TRUE 1.50 1.20e-14 1 1.20e-14 1.20e-14
3 0 FALSE 1.94 1.20e-14 0 1.20e-14 1.20e-14
4 0 TRUE 1.94 1.20e-14 7 1.20e-14 1.20e-14
5 0 FALSE 2.46 1.20e-14 2 1.20e-14 1.20e-14
6 0 TRUE 2.46 1.20e-14 5 1.20e-14 1.20e-14
7 0 FALSE 2.79 1.20e-14 4 1.20e-14 1.20e-14
8 0 TRUE 2.79 1.20e-14 3 1.20e-14 1.20e-14
9 0 FALSE 3.19 1.20e-14 4 1.20e-14 1.20e-14
10 0 TRUE 3.19 1.20e-14 3 1.20e-14 1.20e-14
# i 50 more rows
$Asia$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 0.106 2 0.106 0.106
2 0 TRUE Lincoln Continental 0.106 0 -1.38 -1.38
3 0 FALSE Cadillac Fleetwood 0.106 2 0.106 0.106
4 0 TRUE Cadillac Fleetwood 0.106 0 -1.38 -1.38
5 0 FALSE Merc 450SL 0.106 2 0.106 0.106
6 0 TRUE Merc 450SL 0.106 0 -1.38 -1.38
7 0 FALSE Merc 450SLC 0.106 2 0.106 0.106
8 0 TRUE Merc 450SLC 0.106 0 -1.38 -1.38
9 0 FALSE Camaro Z28 0.106 2 0.106 0.106
10 0 TRUE Camaro Z28 0.106 0 -1.38 -1.38
# i 182 more rows
$Asia$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.0688 30 -0.0688 -0.0688
2 0 TRUE three -0.0688 0 0.453 0.453
3 0 FALSE four -0.0688 8 -0.0688 -0.0688
4 0 TRUE four -0.466 16 0.0563 0.0563
5 0 FALSE five -0.0688 0 -0.0688 -0.0688
6 0 TRUE five -1.06 10 -0.541 -0.541
7 1 FALSE three -0.204 30 -0.204 -0.204
8 1 TRUE three -0.204 0 -0.104 -0.104
9 1 FALSE four -0.340 8 -0.340 -0.340
10 1 TRUE four -0.607 16 -0.507 -0.507
11 1 FALSE five -0.340 0 -0.340 -0.340
12 1 TRUE five -0.905 10 -0.806 -0.806
13 2 FALSE three -0.00554 30 -0.00554 -0.00554
14 2 TRUE three -0.00554 0 -0.628 -0.628
15 2 FALSE four -0.165 8 -0.165 -0.165
16 2 TRUE four -0.228 16 -0.850 -0.850
17 2 FALSE five -0.165 0 -0.165 -0.165
18 2 TRUE five -0.328 10 -0.950 -0.950
$Asia$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 0.0166 6 0.0166 0.0166
2 0 TRUE 1 0.0166 8 -0.107 -0.107
3 0 FALSE 2 0.0166 12 -0.0662 -0.0662
4 0 TRUE 2 -0.00350 7 -0.213 -0.213
5 0 FALSE 3 0.0166 7 0.0203 0.0203
6 0 TRUE 3 -0.00342 2 -0.127 -0.127
7 0 FALSE 4 0.0166 11 -0.0662 -0.0662
8 0 TRUE 4 0.550 5 0.423 0.423
9 0 FALSE 8 0.0166 2 0.0203 0.0203
10 0 TRUE 8 0.550 4 0.340 0.340
# i 20 more rows
$Asia$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 0.0139 0 0.0139 0.0139
2 0 TRUE 1.50 0.0139 1 -0.00322 -0.00322
3 0 FALSE 1.94 0.0139 0 0.00361 0.00361
4 0 TRUE 1.94 0.0138 7 -0.0244 -0.0244
5 0 FALSE 2.46 0.0139 1 -0.00732 -0.00732
6 0 TRUE 2.46 0.0138 6 -0.0245 -0.0245
7 0 FALSE 2.79 0.0139 1 -0.00732 -0.00732
8 0 TRUE 2.79 0.0378 6 0.0207 0.0207
9 0 FALSE 3.19 0.0139 3 0.0139 0.0139
10 0 TRUE 3.19 0.0761 4 0.0487 0.0487
# i 50 more rows
$Asia$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.184 2 -0.184 -0.184
2 0 Cadillac Fleetwood three -0.184 2 -0.184 -0.184
3 0 Merc 450SL three -0.184 2 -0.184 -0.184
4 0 Merc 450SLC three -0.184 2 -0.184 -0.184
5 0 Camaro Z28 three -0.184 2 -0.184 -0.184
6 0 Chrysler Imperial three -0.184 2 0.274 0.274
7 0 Duster 360 three -0.184 2 0.272 0.272
8 0 Merc 450SE three -0.184 2 0.250 0.250
9 0 Hornet Sportabout three -0.184 2 0.250 0.250
10 0 AMC Javelin three -0.184 2 -0.0198 -0.0198
# i 278 more rows
$Asia$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 3.42 0 3.42 3.42
2 0 Cadillac Fleetwood 1 3.42 0 3.42 3.42
3 0 Merc 450SL 1 3.42 0 3.42 3.42
4 0 Merc 450SLC 1 3.42 0 3.42 3.42
5 0 Camaro Z28 1 3.42 0 3.42 3.42
6 0 Chrysler Imperial 1 3.42 0 3.00 3.00
7 0 Duster 360 1 3.42 0 -0.418 -0.418
8 0 Merc 450SE 1 3.42 0 -3.32 -3.32
9 0 Hornet Sportabout 1 3.42 0 -3.74 -3.74
10 0 AMC Javelin 1 3.42 0 -4.12 -4.12
# i 470 more rows
$Asia$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.342 0 0.342 0.342
2 0 Cadillac Fleetwood 1.50 0.342 0 0.342 0.342
3 0 Merc 450SL 1.50 0.342 0 0.342 0.342
4 0 Merc 450SLC 1.50 0.342 0 0.342 0.342
5 0 Camaro Z28 1.50 0.342 0 0.342 0.342
6 0 Chrysler Imperial 1.50 0.342 0 0.342 0.342
7 0 Duster 360 1.50 0.342 0 0.284 0.284
8 0 Merc 450SE 1.50 0.342 0 -0.0324 -0.0324
9 0 Hornet Sportabout 1.50 0.342 0 -0.0439 -0.0439
10 0 AMC Javelin 1.50 0.342 0 -0.0520 -0.0520
# i 950 more rows
$Asia$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.0284 6 0.0284 0.0284
2 0 four 1 0.0284 8 -0.0211 -0.0211
3 0 five 1 0.0284 0 -0.0245 -0.0245
4 0 three 2 0.0284 8 -0.0766 -0.0766
5 0 four 2 0.162 7 0.0573 0.0573
6 0 five 2 0.130 4 0.130 0.130
7 0 three 3 0.0284 7 -0.0211 -0.0211
8 0 four 3 0.193 2 0.140 0.140
9 0 five 3 0.147 0 0.0422 0.0422
10 0 three 4 0.0284 8 -0.0767 -0.0767
# i 35 more rows
$Asia$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 0.0363 0 0.0363 0.0363
2 0 four 1.50 0.0363 0 0.0269 0.0269
3 0 five 1.50 0.0363 1 0.0202 0.0202
4 0 three 1.94 0.0363 0 0.00815 0.00815
5 0 four 1.94 0.0527 6 0.00226 0.00226
6 0 five 1.94 0.0535 1 0.00298 0.00298
7 0 three 2.46 0.0363 1 -0.0141 -0.0141
8 0 four 2.46 0.0599 4 0.00943 0.00943
9 0 five 2.46 0.0786 2 0.0281 0.0281
10 0 three 2.79 0.0363 1 -0.0141 -0.0141
# i 80 more rows
$Asia$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.00454 0 0.00454 0.00454
2 0 2 1.50 0.00454 1 -0.0287 -0.0287
3 0 3 1.50 0.00454 0 -0.0230 -0.0230
4 0 4 1.50 0.00454 0 -0.0329 -0.0329
5 0 8 1.50 0.00454 0 -0.0335 -0.0335
6 0 1 1.94 0.00454 4 -0.00377 -0.00377
7 0 2 1.94 0.0244 3 -0.0171 -0.0171
8 0 3 1.94 0.0244 0 -0.00721 -0.00721
9 0 4 1.94 0.0244 0 -0.0171 -0.0171
10 0 8 1.94 0.0244 0 -0.0171 -0.0171
# i 140 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 1.31e-14 7 1.31e-14 1.31e-14
4 0 16.4 1.31e-14 7 1.31e-14 1.31e-14
5 0 18.7 1.31e-14 7 1.31e-14 1.31e-14
6 0 19.8 1.31e-14 7 1.31e-14 1.31e-14
7 0 21.4 1.31e-14 7 1.31e-14 1.31e-14
8 0 22.9 1.31e-14 7 1.31e-14 1.31e-14
9 0 30.1 1.31e-14 7 1.31e-14 1.31e-14
10 0 33.9 1.31e-14 7 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.31e-14 36 1.31e-14 1.31e-14
2 0 TRUE 1.31e-14 28 1.31e-14 1.31e-14
3 1 FALSE 1.31e-14 36 1.31e-14 1.31e-14
4 1 TRUE 1.31e-14 28 1.31e-14 1.31e-14
5 2 FALSE 1.31e-14 36 1.31e-14 1.31e-14
6 2 TRUE 1.31e-14 28 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 0.0432 38 0.0432 0.0432
2 0 TRUE -0.0632 26 -0.0632 -0.0632
3 1 FALSE -0.0321 38 -0.0321 -0.0321
4 1 TRUE 0.0101 26 0.0101 0.0101
5 2 FALSE -0.0297 38 -0.0297 -0.0297
6 2 TRUE 0.00670 26 0.00670 0.00670
$Europe$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.166 2 0.166 0.166
2 0 Cadillac Fleetwood 0.166 2 0.166 0.166
3 0 Merc 450SL 1.17 2 1.17 1.17
4 0 Merc 450SLC 1.17 2 1.17 1.17
5 0 Camaro Z28 0.166 2 0.166 0.166
6 0 Chrysler Imperial 0.166 2 0.166 0.166
7 0 Duster 360 0.166 2 0.166 0.166
8 0 Merc 450SE 1.17 2 1.17 1.17
9 0 Hornet Sportabout 0.166 2 0.166 0.166
10 0 AMC Javelin 0.166 2 0.166 0.166
# i 86 more rows
$Europe$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three -0.372 30 -0.372 -0.372
2 0 four 0.211 24 0.211 0.211
3 0 five 0.610 10 0.610 0.610
4 1 three -0.106 30 -0.106 -0.106
5 1 four 0.394 24 0.394 0.394
6 1 five 0.594 10 0.594 0.594
7 2 three -0.239 30 -0.239 -0.239
8 2 four 0.136 24 0.136 0.136
9 2 five -0.0638 10 -0.0638 -0.0638
$Europe$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 -0.00549 14 -0.00549 -0.00549
2 0 2 0.00191 19 0.00191 0.00191
3 0 3 0.00191 9 0.00191 0.00191
4 0 4 0.00191 16 0.00191 0.00191
5 0 8 0.00191 6 0.00191 0.00191
6 1 1 -0.00549 18 -0.00549 -0.00549
7 1 2 0.00377 16 0.00377 0.00377
8 1 3 0.00377 13 0.00377 0.00377
9 1 4 0.00377 15 0.00377 0.00377
10 1 8 0.00377 2 0.00377 0.00377
11 2 1 -0.00549 15 -0.00549 -0.00549
12 2 2 0.0000232 13 0.0000232 0.0000232
13 2 3 0.0000232 13 0.0000232 0.0000232
14 2 4 0.0000232 20 0.0000232 0.0000232
15 2 8 0.0000232 3 0.0000232 0.0000232
$Europe$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 -0.0000319 1 -0.0000319 -0.0000319
2 0 1.94 -0.00000854 7 -0.00000854 -0.00000854
3 0 2.46 0.00000364 7 0.00000364 0.00000364
4 0 2.79 0.00000366 7 0.00000366 0.00000366
5 0 3.19 0.00000366 7 0.00000366 0.00000366
6 0 3.44 0.00000366 7 0.00000366 0.00000366
7 0 3.52 0.00000366 7 0.00000366 0.00000366
8 0 3.73 0.00000366 7 0.00000366 0.00000366
9 0 4.05 0.00000366 7 0.00000366 0.00000366
10 0 5.45 0.00000366 7 0.00000366 0.00000366
# i 20 more rows
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 FALSE 1.31e-14 7 1.31e-14 1.31e-14
4 0 16.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
5 0 18.7 FALSE 1.31e-14 3 1.31e-14 1.31e-14
6 0 19.8 FALSE 1.31e-14 5 1.31e-14 1.31e-14
7 0 21.4 FALSE 1.31e-14 4 1.31e-14 1.31e-14
8 0 22.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 FALSE 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 FALSE 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 FALSE 1.31e-14 5 1.31e-14 1.31e-14
4 0 16.4 FALSE 1.31e-14 5 1.31e-14 1.31e-14
5 0 18.7 FALSE 1.31e-14 7 1.31e-14 1.31e-14
6 0 19.8 FALSE 1.31e-14 5 1.31e-14 1.31e-14
7 0 21.4 FALSE 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 FALSE 1.31e-14 5 1.31e-14 1.31e-14
9 0 30.1 FALSE 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 FALSE 1.31e-14 0 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 Lincoln Continental 1.31e-14 2 1.31e-14 1.31e-14
3 0 15.2 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
6 0 19.8 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
8 0 22.9 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
10 0 33.9 Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
# i 950 more rows
$Europe$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 1.31e-14 1 1.31e-14 1.31e-14
2 0 14.4 three 1.31e-14 7 1.31e-14 1.31e-14
3 0 15.2 three 1.31e-14 5 1.31e-14 1.31e-14
4 0 16.4 three 1.31e-14 5 1.31e-14 1.31e-14
5 0 18.7 three 1.31e-14 5 1.31e-14 1.31e-14
6 0 19.8 three 1.31e-14 3 1.31e-14 1.31e-14
7 0 21.4 three 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 three 1.31e-14 3 1.31e-14 1.31e-14
9 0 30.1 three 1.31e-14 0 1.31e-14 1.31e-14
10 0 33.9 three 1.31e-14 0 1.31e-14 1.31e-14
# i 80 more rows
$Europe$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 1 1.31e-14 0 1.31e-14 1.31e-14
3 0 15.2 1 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 1 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 1 1.31e-14 2 1.31e-14 1.31e-14
6 0 19.8 1 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 1 1.31e-14 1 1.31e-14 1.31e-14
8 0 22.9 1 1.31e-14 5 1.31e-14 1.31e-14
9 0 30.1 1 1.31e-14 2 1.31e-14 1.31e-14
10 0 33.9 1 1.31e-14 4 1.31e-14 1.31e-14
# i 140 more rows
$Europe$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
2 0 14.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
3 0 15.2 1.50 1.31e-14 0 1.31e-14 1.31e-14
4 0 16.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
5 0 18.7 1.50 1.31e-14 0 1.31e-14 1.31e-14
6 0 19.8 1.50 1.31e-14 0 1.31e-14 1.31e-14
7 0 21.4 1.50 1.31e-14 0 1.31e-14 1.31e-14
8 0 22.9 1.50 1.31e-14 0 1.31e-14 1.31e-14
9 0 30.1 1.50 1.31e-14 1 1.31e-14 1.31e-14
10 0 33.9 1.50 1.31e-14 0 1.31e-14 1.31e-14
# i 290 more rows
$Europe$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
2 0 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
3 0 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
4 0 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
5 1 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
6 1 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
7 1 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
8 1 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
9 2 FALSE FALSE 1.31e-14 24 1.31e-14 1.31e-14
10 2 TRUE FALSE 1.31e-14 14 1.31e-14 1.31e-14
11 2 FALSE TRUE 1.31e-14 12 1.31e-14 1.31e-14
12 2 TRUE TRUE 1.31e-14 14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 1.31e-14 2 1.31e-14 1.31e-14
2 0 TRUE Lincoln Continental 1.31e-14 0 1.31e-14 1.31e-14
3 0 FALSE Cadillac Fleetwood 1.31e-14 2 1.31e-14 1.31e-14
4 0 TRUE Cadillac Fleetwood 1.31e-14 0 1.31e-14 1.31e-14
5 0 FALSE Merc 450SL 1.31e-14 2 1.31e-14 1.31e-14
6 0 TRUE Merc 450SL 1.31e-14 0 1.31e-14 1.31e-14
7 0 FALSE Merc 450SLC 1.31e-14 2 1.31e-14 1.31e-14
8 0 TRUE Merc 450SLC 1.31e-14 0 1.31e-14 1.31e-14
9 0 FALSE Camaro Z28 1.31e-14 2 1.31e-14 1.31e-14
10 0 TRUE Camaro Z28 1.31e-14 0 1.31e-14 1.31e-14
# i 182 more rows
$Europe$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
2 0 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
3 0 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
4 0 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
5 0 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
6 0 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
7 1 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
8 1 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
9 1 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
10 1 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
11 1 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
12 1 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
13 2 FALSE three 1.31e-14 24 1.31e-14 1.31e-14
14 2 TRUE three 1.31e-14 6 1.31e-14 1.31e-14
15 2 FALSE four 1.31e-14 4 1.31e-14 1.31e-14
16 2 TRUE four 1.31e-14 20 1.31e-14 1.31e-14
17 2 FALSE five 1.31e-14 8 1.31e-14 1.31e-14
18 2 TRUE five 1.31e-14 2 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 1.31e-14 0 1.31e-14 1.31e-14
2 0 TRUE 1 1.31e-14 14 1.31e-14 1.31e-14
3 0 FALSE 2 1.31e-14 10 1.31e-14 1.31e-14
4 0 TRUE 2 1.31e-14 9 1.31e-14 1.31e-14
5 0 FALSE 3 1.31e-14 8 1.31e-14 1.31e-14
6 0 TRUE 3 1.31e-14 1 1.31e-14 1.31e-14
7 0 FALSE 4 1.31e-14 13 1.31e-14 1.31e-14
8 0 TRUE 4 1.31e-14 3 1.31e-14 1.31e-14
9 0 FALSE 8 1.31e-14 5 1.31e-14 1.31e-14
10 0 TRUE 8 1.31e-14 1 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 1.31e-14 0 1.31e-14 1.31e-14
2 0 TRUE 1.50 1.31e-14 1 1.31e-14 1.31e-14
3 0 FALSE 1.94 1.31e-14 0 1.31e-14 1.31e-14
4 0 TRUE 1.94 1.31e-14 7 1.31e-14 1.31e-14
5 0 FALSE 2.46 1.31e-14 2 1.31e-14 1.31e-14
6 0 TRUE 2.46 1.31e-14 5 1.31e-14 1.31e-14
7 0 FALSE 2.79 1.31e-14 4 1.31e-14 1.31e-14
8 0 TRUE 2.79 1.31e-14 3 1.31e-14 1.31e-14
9 0 FALSE 3.19 1.31e-14 4 1.31e-14 1.31e-14
10 0 TRUE 3.19 1.31e-14 3 1.31e-14 1.31e-14
# i 50 more rows
$Europe$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0844 2 -1.57 -1.57
2 0 TRUE Lincoln Continental -0.0844 0 -0.0844 -0.0844
3 0 FALSE Cadillac Fleetwood -0.0844 2 -1.57 -1.57
4 0 TRUE Cadillac Fleetwood -0.0844 0 -0.0844 -0.0844
5 0 FALSE Merc 450SL -0.0844 2 -1.57 -1.57
6 0 TRUE Merc 450SL -0.0844 0 -0.0844 -0.0844
7 0 FALSE Merc 450SLC -0.0844 2 -1.57 -1.57
8 0 TRUE Merc 450SLC -0.0844 0 -0.0844 -0.0844
9 0 FALSE Camaro Z28 -0.0844 2 -1.57 -1.57
10 0 TRUE Camaro Z28 -0.0844 0 -0.0844 -0.0844
# i 182 more rows
$Europe$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three -0.00407 30 0.386 0.386
2 0 TRUE three -0.00407 0 0.427 0.427
3 0 FALSE four -0.00407 8 0.386 0.386
4 0 TRUE four -0.177 16 0.254 0.254
5 0 FALSE five -0.00407 0 0.386 0.386
6 0 TRUE five 0.226 10 0.657 0.657
7 1 FALSE three -0.0677 30 -0.0254 -0.0254
8 1 TRUE three -0.0677 0 -0.212 -0.212
9 1 FALSE four -0.187 8 -0.145 -0.145
10 1 TRUE four -0.241 16 -0.385 -0.385
11 1 FALSE five -0.187 0 -0.145 -0.145
12 1 TRUE five -0.0395 10 -0.183 -0.183
13 2 FALSE three -0.0677 30 -0.500 -0.500
14 2 TRUE three -0.0677 0 -0.355 -0.355
15 2 FALSE four -0.176 8 -0.608 -0.608
16 2 TRUE four -0.175 16 -0.463 -0.463
17 2 FALSE five -0.176 0 -0.608 -0.608
18 2 TRUE five -0.0753 10 -0.363 -0.363
$Europe$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0166 6 -0.144 -0.144
2 0 TRUE 1 -0.0166 8 -0.0129 -0.0129
3 0 FALSE 2 -0.0166 12 -0.226 -0.226
4 0 TRUE 2 0.00350 7 0.00350 0.00350
5 0 FALSE 3 -0.0166 7 -0.140 -0.140
6 0 TRUE 3 0.00342 2 -0.0793 -0.0793
7 0 FALSE 4 -0.0166 11 -0.226 -0.226
8 0 TRUE 4 -0.550 5 -0.546 -0.546
9 0 FALSE 8 -0.0166 2 -0.140 -0.140
10 0 TRUE 8 -0.550 4 -0.633 -0.633
# i 20 more rows
$Europe$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 -0.0139 0 -0.0310 -0.0310
2 0 TRUE 1.50 -0.0139 1 -0.0138 -0.0138
3 0 FALSE 1.94 -0.0139 0 -0.0412 -0.0412
4 0 TRUE 1.94 -0.0138 7 -0.0350 -0.0350
5 0 FALSE 2.46 -0.0139 1 -0.0522 -0.0522
6 0 TRUE 2.46 -0.0138 6 -0.0350 -0.0350
7 0 FALSE 2.79 -0.0139 1 -0.0522 -0.0522
8 0 TRUE 2.79 -0.0378 6 -0.0378 -0.0378
9 0 FALSE 3.19 -0.0139 3 -0.0309 -0.0309
10 0 TRUE 3.19 -0.0761 4 -0.0973 -0.0973
# i 50 more rows
$Europe$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three 0.301 2 0.301 0.301
2 0 Cadillac Fleetwood three 0.301 2 0.301 0.301
3 0 Merc 450SL three 0.301 2 0.301 0.301
4 0 Merc 450SLC three 0.301 2 0.301 0.301
5 0 Camaro Z28 three 0.301 2 0.301 0.301
6 0 Chrysler Imperial three 0.301 2 0.759 0.759
7 0 Duster 360 three 0.301 2 0.729 0.729
8 0 Merc 450SE three 0.301 2 0.235 0.235
9 0 Hornet Sportabout three 0.301 2 0.794 0.794
10 0 AMC Javelin three 0.301 2 0.964 0.964
# i 278 more rows
$Europe$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 -3.89 0 -3.89 -3.89
2 0 Cadillac Fleetwood 1 -3.89 0 -3.89 -3.89
3 0 Merc 450SL 1 -3.89 0 -3.89 -3.89
4 0 Merc 450SLC 1 -3.89 0 -3.89 -3.89
5 0 Camaro Z28 1 -3.89 0 -3.89 -3.89
6 0 Chrysler Imperial 1 -3.89 0 -5.45 -5.45
7 0 Duster 360 1 -3.89 0 -8.67 -8.67
8 0 Merc 450SE 1 -3.89 0 -11.1 -11.1
9 0 Hornet Sportabout 1 -3.89 0 -11.1 -11.1
10 0 AMC Javelin 1 -3.89 0 -11.4 -11.4
# i 470 more rows
$Europe$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 -0.403 0 -0.403 -0.403
2 0 Cadillac Fleetwood 1.50 -0.403 0 -0.403 -0.403
3 0 Merc 450SL 1.50 -0.403 0 -0.403 -0.403
4 0 Merc 450SLC 1.50 -0.403 0 -0.403 -0.403
5 0 Camaro Z28 1.50 -0.403 0 -0.403 -0.403
6 0 Chrysler Imperial 1.50 -0.403 0 -0.460 -0.460
7 0 Duster 360 1.50 -0.403 0 -0.555 -0.555
8 0 Merc 450SE 1.50 -0.403 0 -0.778 -0.778
9 0 Hornet Sportabout 1.50 -0.403 0 -0.789 -0.789
10 0 AMC Javelin 1.50 -0.403 0 -0.797 -0.797
# i 950 more rows
$Europe$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 0.00218 6 -0.105 -0.105
2 0 four 1 0.00218 8 -0.155 -0.155
3 0 five 1 0.00218 0 -0.158 -0.158
4 0 three 2 0.00218 8 -0.210 -0.210
5 0 four 2 0.00237 7 -0.210 -0.210
6 0 five 2 0.0348 4 -0.0727 -0.0727
7 0 three 3 0.00218 7 -0.155 -0.155
8 0 four 3 0.00237 2 -0.158 -0.158
9 0 five 3 0.0510 0 -0.161 -0.161
10 0 three 4 0.00218 8 -0.210 -0.210
# i 35 more rows
$Europe$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.00213 0 -0.0423 -0.0423
2 0 four 1.50 -0.00213 0 -0.0517 -0.0517
3 0 five 1.50 -0.00213 1 -0.0584 -0.0584
4 0 three 1.94 -0.00213 0 -0.0705 -0.0705
5 0 four 1.94 -0.00210 6 -0.0927 -0.0927
6 0 five 1.94 -0.00282 1 -0.0935 -0.0935
7 0 three 2.46 -0.00213 1 -0.0928 -0.0928
8 0 four 2.46 -0.00208 4 -0.0927 -0.0927
9 0 five 2.46 -0.0208 2 -0.111 -0.111
10 0 three 2.79 -0.00213 1 -0.0928 -0.0928
# i 80 more rows
$Europe$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 -0.00454 0 -0.0278 -0.0278
2 0 2 1.50 -0.00454 1 -0.0377 -0.0377
3 0 3 1.50 -0.00454 0 -0.0420 -0.0420
4 0 4 1.50 -0.00454 0 -0.00941 -0.00941
5 0 8 1.50 -0.00454 0 -0.0426 -0.0426
6 0 1 1.94 -0.00454 4 -0.0361 -0.0361
7 0 2 1.94 -0.0244 3 -0.0659 -0.0659
8 0 3 1.94 -0.0244 0 -0.0659 -0.0659
9 0 4 1.94 -0.0244 0 -0.0327 -0.0327
10 0 8 1.94 -0.0244 0 -0.0659 -0.0659
# i 140 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 30 x 6
.it mpg .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 2.64e-14 7 2.64e-14 2.64e-14
4 0 16.4 2.64e-14 7 2.64e-14 2.64e-14
5 0 18.7 2.64e-14 7 2.64e-14 2.64e-14
6 0 19.8 2.64e-14 7 2.64e-14 2.64e-14
7 0 21.4 2.64e-14 7 2.64e-14 2.64e-14
8 0 22.9 2.64e-14 7 2.64e-14 2.64e-14
9 0 30.1 2.64e-14 7 2.64e-14 2.64e-14
10 0 33.9 2.64e-14 7 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d1$vs
# A tibble: 6 x 6
.it vs .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 2.64e-14 36 2.64e-14 2.64e-14
2 0 TRUE 2.64e-14 28 2.64e-14 2.64e-14
3 1 FALSE 2.64e-14 36 2.64e-14 2.64e-14
4 1 TRUE 2.64e-14 28 2.64e-14 2.64e-14
5 2 FALSE 2.64e-14 36 2.64e-14 2.64e-14
6 2 TRUE 2.64e-14 28 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 6 x 6
.it am .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.52e-10 38 1.52e-10 1.52e-10
2 0 TRUE -2.23e-10 26 -2.23e-10 -2.23e-10
3 1 FALSE -9.71e-10 38 -9.71e-10 -9.71e-10
4 1 TRUE 7.39e-10 26 7.39e-10 7.39e-10
5 2 FALSE -8.52e-10 38 -8.52e-10 -8.52e-10
6 2 TRUE -4.71e-11 26 -4.71e-11 -4.71e-11
$`North America`$d1$model
# A tibble: 96 x 6
.it model .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 0.139 2 0.139 0.139
2 0 Cadillac Fleetwood 0.139 2 0.139 0.139
3 0 Merc 450SL -0.861 2 -0.861 -0.861
4 0 Merc 450SLC -0.861 2 -0.861 -0.861
5 0 Camaro Z28 0.139 2 0.139 0.139
6 0 Chrysler Imperial 0.139 2 0.139 0.139
7 0 Duster 360 0.139 2 0.139 0.139
8 0 Merc 450SE -0.861 2 -0.861 -0.861
9 0 Hornet Sportabout 0.139 2 0.139 0.139
10 0 AMC Javelin 0.139 2 0.139 0.139
# i 86 more rows
$`North America`$d1$gear
# A tibble: 9 x 6
.it gear .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 three 0.254 30 0.254 0.254
2 0 four -0.283 24 -0.283 -0.283
3 0 five -0.0843 10 -0.0843 -0.0843
4 1 three 0.0526 30 0.0526 0.0526
5 1 four -0.232 24 -0.232 -0.232
6 1 five -0.133 10 -0.133 -0.133
7 2 three 0.0544 30 0.0544 0.0544
8 2 four -0.232 24 -0.232 -0.232
9 2 five 0.0679 10 0.0679 0.0679
$`North America`$d1$carb
# A tibble: 15 x 6
.it carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.08e-7 14 1.08e-7 1.08e-7
2 0 2 -3.71e-8 19 -3.71e-8 -3.71e-8
3 0 3 -3.71e-8 9 -3.71e-8 -3.71e-8
4 0 4 -3.71e-8 16 -3.71e-8 -3.71e-8
5 0 8 -4.13e-8 6 -4.13e-8 -4.13e-8
6 1 1 1.08e-7 18 1.08e-7 1.08e-7
7 1 2 -7.21e-8 16 -7.21e-8 -7.21e-8
8 1 3 -7.21e-8 13 -7.21e-8 -7.21e-8
9 1 4 -7.21e-8 15 -7.21e-8 -7.21e-8
10 1 8 -8.38e-8 2 -8.38e-8 -8.38e-8
11 2 1 1.08e-7 15 1.08e-7 1.08e-7
12 2 2 1.40e-9 13 1.40e-9 1.40e-9
13 2 3 1.40e-9 13 1.40e-9 1.40e-9
14 2 4 1.40e-9 20 1.40e-9 1.40e-9
15 2 8 1.17e-9 3 1.17e-9 1.17e-9
$`North America`$d1$wt
# A tibble: 30 x 6
.it wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1.50 6.60e-10 1 6.60e-10 6.60e-10
2 0 1.94 -8.90e-12 7 -8.90e-12 -8.90e-12
3 0 2.46 -4.95e-11 7 -4.95e-11 -4.95e-11
4 0 2.79 -4.95e-11 7 -4.95e-11 -4.95e-11
5 0 3.19 -4.95e-11 7 -4.95e-11 -4.95e-11
6 0 3.44 -4.87e-11 7 -4.87e-11 -4.87e-11
7 0 3.52 -4.87e-11 7 -4.87e-11 -4.87e-11
8 0 3.73 -4.87e-11 7 -4.87e-11 -4.87e-11
9 0 4.05 -4.87e-11 7 -4.87e-11 -4.87e-11
10 0 5.45 -4.87e-11 7 -4.87e-11 -4.87e-11
# i 20 more rows
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 60 x 7
.it mpg vs .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 FALSE 2.64e-14 7 2.64e-14 2.64e-14
4 0 16.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
5 0 18.7 FALSE 2.64e-14 3 2.64e-14 2.64e-14
6 0 19.8 FALSE 2.64e-14 5 2.64e-14 2.64e-14
7 0 21.4 FALSE 2.64e-14 4 2.64e-14 2.64e-14
8 0 22.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 FALSE 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`mpg:am`
# A tibble: 60 x 7
.it mpg am .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 FALSE 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 FALSE 2.64e-14 5 2.64e-14 2.64e-14
4 0 16.4 FALSE 2.64e-14 5 2.64e-14 2.64e-14
5 0 18.7 FALSE 2.64e-14 7 2.64e-14 2.64e-14
6 0 19.8 FALSE 2.64e-14 5 2.64e-14 2.64e-14
7 0 21.4 FALSE 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 FALSE 2.64e-14 5 2.64e-14 2.64e-14
9 0 30.1 FALSE 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 FALSE 2.64e-14 0 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`mpg:model`
# A tibble: 960 x 7
.it mpg model .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 Lincoln Continental 2.64e-14 2 2.64e-14 2.64e-14
3 0 15.2 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
6 0 19.8 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
8 0 22.9 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
10 0 33.9 Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
# i 950 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 90 x 7
.it mpg gear .y_composite .n .y_distinct .y
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 three 2.64e-14 1 2.64e-14 2.64e-14
2 0 14.4 three 2.64e-14 7 2.64e-14 2.64e-14
3 0 15.2 three 2.64e-14 5 2.64e-14 2.64e-14
4 0 16.4 three 2.64e-14 5 2.64e-14 2.64e-14
5 0 18.7 three 2.64e-14 5 2.64e-14 2.64e-14
6 0 19.8 three 2.64e-14 3 2.64e-14 2.64e-14
7 0 21.4 three 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 three 2.64e-14 3 2.64e-14 2.64e-14
9 0 30.1 three 2.64e-14 0 2.64e-14 2.64e-14
10 0 33.9 three 2.64e-14 0 2.64e-14 2.64e-14
# i 80 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 150 x 7
.it mpg carb .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 1 2.64e-14 0 2.64e-14 2.64e-14
3 0 15.2 1 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 1 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 1 2.64e-14 2 2.64e-14 2.64e-14
6 0 19.8 1 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 1 2.64e-14 1 2.64e-14 2.64e-14
8 0 22.9 1 2.64e-14 5 2.64e-14 2.64e-14
9 0 30.1 1 2.64e-14 2 2.64e-14 2.64e-14
10 0 33.9 1 2.64e-14 4 2.64e-14 2.64e-14
# i 140 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 300 x 7
.it mpg wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 10.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
2 0 14.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
3 0 15.2 1.50 2.64e-14 0 2.64e-14 2.64e-14
4 0 16.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
5 0 18.7 1.50 2.64e-14 0 2.64e-14 2.64e-14
6 0 19.8 1.50 2.64e-14 0 2.64e-14 2.64e-14
7 0 21.4 1.50 2.64e-14 0 2.64e-14 2.64e-14
8 0 22.9 1.50 2.64e-14 0 2.64e-14 2.64e-14
9 0 30.1 1.50 2.64e-14 1 2.64e-14 2.64e-14
10 0 33.9 1.50 2.64e-14 0 2.64e-14 2.64e-14
# i 290 more rows
$`North America`$d2$`vs:am`
# A tibble: 12 x 7
.it vs am .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
2 0 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
3 0 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
4 0 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
5 1 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
6 1 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
7 1 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
8 1 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
9 2 FALSE FALSE 2.64e-14 24 2.64e-14 2.64e-14
10 2 TRUE FALSE 2.64e-14 14 2.64e-14 2.64e-14
11 2 FALSE TRUE 2.64e-14 12 2.64e-14 2.64e-14
12 2 TRUE TRUE 2.64e-14 14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 192 x 7
.it vs model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental 2.64e-14 2 2.64e-14 2.64e-14
2 0 TRUE Lincoln Continental 2.64e-14 0 2.64e-14 2.64e-14
3 0 FALSE Cadillac Fleetwood 2.64e-14 2 2.64e-14 2.64e-14
4 0 TRUE Cadillac Fleetwood 2.64e-14 0 2.64e-14 2.64e-14
5 0 FALSE Merc 450SL 2.64e-14 2 2.64e-14 2.64e-14
6 0 TRUE Merc 450SL 2.64e-14 0 2.64e-14 2.64e-14
7 0 FALSE Merc 450SLC 2.64e-14 2 2.64e-14 2.64e-14
8 0 TRUE Merc 450SLC 2.64e-14 0 2.64e-14 2.64e-14
9 0 FALSE Camaro Z28 2.64e-14 2 2.64e-14 2.64e-14
10 0 TRUE Camaro Z28 2.64e-14 0 2.64e-14 2.64e-14
# i 182 more rows
$`North America`$d2$`vs:gear`
# A tibble: 18 x 7
.it vs gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
2 0 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
3 0 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
4 0 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
5 0 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
6 0 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
7 1 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
8 1 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
9 1 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
10 1 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
11 1 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
12 1 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
13 2 FALSE three 2.64e-14 24 2.64e-14 2.64e-14
14 2 TRUE three 2.64e-14 6 2.64e-14 2.64e-14
15 2 FALSE four 2.64e-14 4 2.64e-14 2.64e-14
16 2 TRUE four 2.64e-14 20 2.64e-14 2.64e-14
17 2 FALSE five 2.64e-14 8 2.64e-14 2.64e-14
18 2 TRUE five 2.64e-14 2 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 30 x 7
.it vs carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 2.64e-14 0 2.64e-14 2.64e-14
2 0 TRUE 1 2.64e-14 14 2.64e-14 2.64e-14
3 0 FALSE 2 2.64e-14 10 2.64e-14 2.64e-14
4 0 TRUE 2 2.64e-14 9 2.64e-14 2.64e-14
5 0 FALSE 3 2.64e-14 8 2.64e-14 2.64e-14
6 0 TRUE 3 2.64e-14 1 2.64e-14 2.64e-14
7 0 FALSE 4 2.64e-14 13 2.64e-14 2.64e-14
8 0 TRUE 4 2.64e-14 3 2.64e-14 2.64e-14
9 0 FALSE 8 2.64e-14 5 2.64e-14 2.64e-14
10 0 TRUE 8 2.64e-14 1 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`vs:wt`
# A tibble: 60 x 7
.it vs wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 2.64e-14 0 2.64e-14 2.64e-14
2 0 TRUE 1.50 2.64e-14 1 2.64e-14 2.64e-14
3 0 FALSE 1.94 2.64e-14 0 2.64e-14 2.64e-14
4 0 TRUE 1.94 2.64e-14 7 2.64e-14 2.64e-14
5 0 FALSE 2.46 2.64e-14 2 2.64e-14 2.64e-14
6 0 TRUE 2.46 2.64e-14 5 2.64e-14 2.64e-14
7 0 FALSE 2.79 2.64e-14 4 2.64e-14 2.64e-14
8 0 TRUE 2.79 2.64e-14 3 2.64e-14 2.64e-14
9 0 FALSE 3.19 2.64e-14 4 2.64e-14 2.64e-14
10 0 TRUE 3.19 2.64e-14 3 2.64e-14 2.64e-14
# i 50 more rows
$`North America`$d2$`am:model`
# A tibble: 192 x 7
.it am model .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE Lincoln Continental -0.0218 2 -0.0218 -0.0218
2 0 TRUE Lincoln Continental -0.0218 0 -1.51 -1.51
3 0 FALSE Cadillac Fleetwood -0.0218 2 -0.0218 -0.0218
4 0 TRUE Cadillac Fleetwood -0.0218 0 -1.51 -1.51
5 0 FALSE Merc 450SL -0.0218 2 -0.0218 -0.0218
6 0 TRUE Merc 450SL -0.0218 0 -1.51 -1.51
7 0 FALSE Merc 450SLC -0.0218 2 -0.0218 -0.0218
8 0 TRUE Merc 450SLC -0.0218 0 -1.51 -1.51
9 0 FALSE Camaro Z28 -0.0218 2 -0.0218 -0.0218
10 0 TRUE Camaro Z28 -0.0218 0 -1.51 -1.51
# i 182 more rows
$`North America`$d2$`am:gear`
# A tibble: 18 x 7
.it am gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE three 0.0729 30 0.205 0.205
2 0 TRUE three 0.0729 0 0.894 0.894
3 0 FALSE four 0.0729 8 0.205 0.205
4 0 TRUE four 0.643 16 1.46 1.46
5 0 FALSE five 0.0729 0 0.205 0.205
6 0 TRUE five 0.838 10 1.66 1.66
7 1 FALSE three 0.271 30 0.329 0.329
8 1 TRUE three 0.271 0 0.170 0.170
9 1 FALSE four 0.527 8 0.585 0.585
10 1 TRUE four 0.848 16 0.746 0.746
11 1 FALSE five 0.527 0 0.585 0.585
12 1 TRUE five 0.945 10 0.843 0.843
13 2 FALSE three 0.0732 30 -0.117 -0.117
14 2 TRUE three 0.0732 0 -0.646 -0.646
15 2 FALSE four 0.340 8 0.150 0.150
16 2 TRUE four 0.403 16 -0.317 -0.317
17 2 FALSE five 0.340 0 0.150 0.150
18 2 TRUE five 0.403 10 -0.317 -0.317
$`North America`$d2$`am:carb`
# A tibble: 30 x 7
.it am carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1 -0.0000000460 6 0.00370 0.00370
2 0 TRUE 1 -0.0000000460 8 -0.123 -0.123
3 0 FALSE 2 -0.0000000460 12 -0.0828 -0.0828
4 0 TRUE 2 -0.000000439 7 -0.127 -0.127
5 0 FALSE 3 -0.0000000460 7 0.00369 0.00369
6 0 TRUE 3 -0.000000439 2 -0.210 -0.210
7 0 FALSE 4 -0.0000000460 11 -0.0000000460 -0.0000000460
8 0 TRUE 4 -0.000000446 5 -0.123 -0.123
9 0 FALSE 8 -0.0000000460 2 -0.0828 -0.0828
10 0 TRUE 8 -0.000000447 4 -0.210 -0.210
# i 20 more rows
$`North America`$d2$`am:wt`
# A tibble: 60 x 7
.it am wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 FALSE 1.50 9.93e-11 0 1.17e- 5 1.17e- 5
2 0 TRUE 1.50 9.93e-11 1 -1.71e- 2 -1.71e- 2
3 0 FALSE 1.94 9.93e-11 0 -2.12e- 2 -2.12e- 2
4 0 TRUE 1.94 -5.71e-10 7 -3.83e- 2 -3.83e- 2
5 0 FALSE 2.46 9.93e-11 1 -2.12e- 2 -2.12e- 2
6 0 TRUE 2.46 -6.18e-10 6 -3.83e- 2 -3.83e- 2
7 0 FALSE 2.79 9.93e-11 1 9.93e-11 9.93e-11
8 0 TRUE 2.79 7.83e-10 6 -1.71e- 2 -1.71e- 2
9 0 FALSE 3.19 9.93e-11 3 -1.03e- 2 -1.03e- 2
10 0 TRUE 3.19 7.86e-10 4 -3.83e- 2 -3.83e- 2
# i 50 more rows
$`North America`$d2$`model:gear`
# A tibble: 288 x 7
.it model gear .y_composite .n .y_distinct .y
<dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental three -0.117 2 -0.117 -0.117
2 0 Cadillac Fleetwood three -0.117 2 -0.117 -0.117
3 0 Merc 450SL three -0.117 2 -0.117 -0.117
4 0 Merc 450SLC three -0.117 2 -0.117 -0.117
5 0 Camaro Z28 three -0.117 2 -0.117 -0.117
6 0 Chrysler Imperial three -0.117 2 0.339 0.339
7 0 Duster 360 three -0.117 2 0.317 0.317
8 0 Merc 450SE three -0.117 2 0.317 0.317
9 0 Hornet Sportabout three -0.117 2 0.434 0.434
10 0 AMC Javelin three -0.117 2 0.547 0.547
# i 278 more rows
$`North America`$d2$`model:carb`
# A tibble: 480 x 7
.it model carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1 0.471 0 0.471 0.471
2 0 Cadillac Fleetwood 1 0.471 0 0.471 0.471
3 0 Merc 450SL 1 0.471 0 0.471 0.471
4 0 Merc 450SLC 1 0.471 0 0.471 0.471
5 0 Camaro Z28 1 0.471 0 0.471 0.471
6 0 Chrysler Imperial 1 0.471 0 -1.53 -1.53
7 0 Duster 360 1 0.471 0 -5.55 -5.55
8 0 Merc 450SE 1 0.471 0 -6.69 -6.69
9 0 Hornet Sportabout 1 0.471 0 -6.97 -6.97
10 0 AMC Javelin 1 0.471 0 -7.07 -7.07
# i 470 more rows
$`North America`$d2$`model:wt`
# A tibble: 960 x 7
.it model wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 Lincoln Continental 1.50 0.0603 0 0.0603 0.0603
2 0 Cadillac Fleetwood 1.50 0.0603 0 0.0603 0.0603
3 0 Merc 450SL 1.50 0.0603 0 0.0603 0.0603
4 0 Merc 450SLC 1.50 0.0603 0 0.0603 0.0603
5 0 Camaro Z28 1.50 0.0603 0 0.0602 0.0602
6 0 Chrysler Imperial 1.50 0.0603 0 0.00250 0.00250
7 0 Duster 360 1.50 0.0603 0 -0.252 -0.252
8 0 Merc 450SE 1.50 0.0603 0 -0.318 -0.318
9 0 Hornet Sportabout 1.50 0.0603 0 -0.334 -0.334
10 0 AMC Javelin 1.50 0.0603 0 -0.334 -0.334
# i 950 more rows
$`North America`$d2$`gear:carb`
# A tibble: 45 x 7
.it gear carb .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1 -0.0306 6 -0.108 -0.108
2 0 four 1 -0.0306 8 -0.157 -0.157
3 0 five 1 -0.0306 0 -0.160 -0.160
4 0 three 2 -0.0306 8 -0.213 -0.213
5 0 four 2 -0.165 7 -0.347 -0.347
6 0 five 2 -0.165 4 -0.242 -0.242
7 0 three 3 -0.0306 7 -0.157 -0.157
8 0 four 3 -0.195 2 -0.325 -0.325
9 0 five 3 -0.198 0 -0.380 -0.380
10 0 three 4 -0.0306 8 -0.213 -0.213
# i 35 more rows
$`North America`$d2$`gear:wt`
# A tibble: 90 x 7
.it gear wt .y_composite .n .y_distinct .y
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 three 1.50 -0.0342 0 -0.0903 -0.0903
2 0 four 1.50 -0.0342 0 -0.0997 -0.0997
3 0 five 1.50 -0.0342 1 -0.106 -0.106
4 0 three 1.94 -0.0342 0 -0.119 -0.119
5 0 four 1.94 -0.0506 6 -0.157 -0.157
6 0 five 1.94 -0.0506 1 -0.157 -0.157
7 0 three 2.46 -0.0342 1 -0.141 -0.141
8 0 four 2.46 -0.0578 4 -0.164 -0.164
9 0 five 2.46 -0.0578 2 -0.164 -0.164
10 0 three 2.79 -0.0342 1 -0.141 -0.141
# i 80 more rows
$`North America`$d2$`carb:wt`
# A tibble: 150 x 7
.it carb wt .y_composite .n .y_distinct .y
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 1.50 0.0000000171 0 -0.0332 -0.0332
2 0 2 1.50 0.0000000171 1 -0.00425 -0.00425
3 0 3 1.50 0.0000000171 0 -0.0375 -0.0375
4 0 4 1.50 0.0000000171 0 -0.0281 -0.0281
5 0 8 1.50 0.0000000171 0 -0.0381 -0.0381
6 0 1 1.94 0.0000000171 4 -0.0415 -0.0415
7 0 2 1.94 0.000000145 3 -0.00831 -0.00831
8 0 3 1.94 0.000000145 0 -0.0415 -0.0415
9 0 4 1.94 0.000000145 0 -0.0316 -0.0316
10 0 8 1.94 0.000000145 0 -0.0415 -0.0415
# i 140 more rows
Code
get(cat_cars_ale, stats = "estimate")
Output
$Asia
$Asia$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 0.0218 -0.00838 0.0309 38.9 -50 31.2
4 model 1.17 -1.94 1.05 43.0 -50 40.6
5 gear 0.140 -0.232 0.119 37.7 -50 31.2
6 carb 0.00172 -0.00189 0.00549 40.9 -50 31.2
7 wt 0.0000118 -0.0000118 0.0000319 47.8 -50 31.2
$Asia$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
$Europe
$Europe$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 0.0218 -0.0309 0.00838 32.2 -50 6.25
4 model 2.27 -0.680 4.28 45.9 -21.9 50
5 gear 0.232 -0.172 0.365 30.2 -50 6.25
6 carb 0.00172 -0.00549 0.00189 27.4 -50 6.25
7 wt 0.0000118 -0.0000319 0.0000118 10.7 -50 6.25
$Europe$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
$`North America`
$`North America`$d1
# A tibble: 7 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 0 0 0 0 0 0
2 vs 0 0 0 0 0 0
3 am 7.01e-10 -9.12e-10 3.46e-10 41.7 -50 -20.3
4 model 1.48e+ 0 -2.59e+ 0 -1.11e- 1 43.0 -50 -18.8
5 gear 1.28e- 1 -2.32e- 1 6.02e- 2 29.5 -50 12.5
6 carb 3.40e- 8 -4.13e- 8 1.08e- 7 20.4 -20.3 12.5
7 wt 3.83e-10 3.53e-10 6.60e-10 8.06 7.81 9.38
$`North America`$d2
# A tibble: 21 x 7
term aled aler_min aler_max naled naler_min naler_max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg:vs 0 0 0 0 0 0
2 mpg:am 0 0 0 0 0 0
3 mpg:model 0 0 0 0 0 0
4 mpg:gear 0 0 0 0 0 0
5 mpg:carb 0 0 0 0 0 0
6 mpg:wt 0 0 0 0 0 0
7 vs:am 0 0 0 0 0 0
8 vs:model 0 0 0 0 0 0
9 vs:gear 0 0 0 0 0 0
10 vs:carb 0 0 0 0 0 0
# i 11 more rows
Code
get(cat_cars_ale, cats = c("Asia", "Europe"))
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 0.214 0.106 0.214 0.106 0.415
2 TRUE Lincoln Continental 0 0.214 -1.28 0.214 0.512 1.46
3 FALSE Cadillac Fleetwood 2 0.323 0.106 0.323 0.106 0.723
4 TRUE Cadillac Fleetwood 0 0.323 -1.27 0.323 0.837 1.48
5 FALSE Merc 450SL 2 0.0915 -0.552 0.0915 0.106 0.723
6 TRUE Merc 450SL 0 0.0915 -1.27 0.0915 0.817 0.836
7 FALSE Merc 450SLC 2 0.0915 -0.552 0.0915 0.106 0.723
8 TRUE Merc 450SLC 0 0.0915 -1.27 0.0915 0.817 0.836
9 FALSE Camaro Z28 2 0.0910 -0.552 0.0910 0.106 0.721
10 TRUE Camaro Z28 0 0.0910 -1.27 0.0910 0.817 0.834
# i 54 more rows
$Asia$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0927 -0.197 -0.0927 -0.0688 -0.00870
2 TRUE three 0 -0.0927 -0.601 -0.0927 -0.104 0.425
3 FALSE four 8 -0.191 -0.332 -0.191 -0.165 -0.0736
4 TRUE four 16 -0.433 -0.832 -0.433 -0.507 0.0281
5 FALSE five 0 -0.191 -0.332 -0.191 -0.165 -0.0736
6 TRUE five 10 -0.766 -0.942 -0.766 -0.806 -0.555
$Asia$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 0.0166 0.0166 0.0166 0.0166 0.0166
2 TRUE 1 8 0.0166 -0.101 0.0166 0.0166 0.134
3 FALSE 2 12 0.0134 -0.0621 0.0134 0.0163 0.0863
4 TRUE 2 7 -0.000253 -0.203 -0.000253 0.00468 0.198
5 FALSE 3 7 0.0134 0.00415 0.0134 0.0163 0.0201
6 TRUE 3 2 -0.000226 -0.120 -0.000226 0.00468 0.116
7 FALSE 4 11 0.190 -0.0514 0.190 0.229 0.399
8 TRUE 4 5 0.454 0.357 0.454 0.423 0.579
9 FALSE 8 2 0.190 0.0307 0.190 0.229 0.317
10 TRUE 8 4 0.454 0.341 0.454 0.353 0.654
$Asia$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 0.0139 0.0139 0.0139 0.0139 0.0139
2 TRUE 1.50 1 0.0139 -0.00237 0.0139 0.0139 0.0301
3 FALSE 1.94 0 0.0139 0.00413 0.0139 0.0139 0.0236
4 TRUE 1.94 7 0.0138 -0.0225 0.0138 0.0138 0.0502
5 FALSE 2.46 1 0.0138 -0.00627 0.0138 0.0139 0.0339
6 TRUE 2.46 6 0.0138 -0.0225 0.0138 0.0138 0.0502
7 FALSE 2.79 1 0.0314 -0.00483 0.0314 0.0426 0.0582
8 TRUE 2.79 6 0.0538 0.0227 0.0538 0.0605 0.0793
9 FALSE 3.19 3 0.0481 0.0157 0.0481 0.0497 0.0792
10 TRUE 3.19 4 0.100 0.0512 0.100 0.0987 0.151
11 FALSE 3.44 7 0.0481 -0.00341 0.0481 0.0709 0.0803
12 TRUE 3.44 0 0.109 0.0651 0.109 0.0987 0.161
13 FALSE 3.52 7 0.0481 -0.00341 0.0481 0.0709 0.0803
14 TRUE 3.52 0 0.113 0.0772 0.113 0.0987 0.161
15 FALSE 3.73 5 0.0481 0.0157 0.0481 0.0497 0.0792
16 TRUE 3.73 2 0.113 0.0973 0.113 0.0987 0.141
17 FALSE 4.05 7 0.0481 -0.00341 0.0481 0.0709 0.0803
18 TRUE 4.05 0 0.113 0.0772 0.113 0.0987 0.161
19 FALSE 5.45 7 0.0481 -0.00341 0.0481 0.0709 0.0803
20 TRUE 5.45 0 0.113 0.0772 0.113 0.0987 0.161
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`am:model`
# A tibble: 64 x 8
am.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continental 2 -0.193 -1.51 -0.193 -0.327 1.24
2 TRUE Lincoln Continental 0 -0.193 -0.393 -0.193 -0.0844 -0.0844
3 FALSE Cadillac Fleetwood 2 -0.301 -1.52 -0.301 -0.652 1.22
4 TRUE Cadillac Fleetwood 0 -0.301 -0.701 -0.301 -0.0844 -0.0844
5 FALSE Merc 450SL 2 -0.0689 -1.52 -0.0689 -0.652 1.88
6 TRUE Merc 450SL 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
7 FALSE Merc 450SLC 2 -0.0689 -1.52 -0.0689 -0.652 1.88
8 TRUE Merc 450SLC 0 -0.0689 -0.701 -0.0689 -0.0844 0.576
9 FALSE Camaro Z28 2 -0.0683 -1.52 -0.0683 -0.650 1.88
10 TRUE Camaro Z28 0 -0.0683 -0.699 -0.0683 -0.0844 0.576
# i 54 more rows
$Europe$d2$`am:gear`
# A tibble: 6 x 8
am.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 30 -0.0465 -0.476 -0.0465 -0.0254 0.365
2 TRUE three 0 -0.0465 -0.348 -0.0465 -0.212 0.395
3 FALSE four 8 -0.122 -0.584 -0.122 -0.145 0.359
4 TRUE four 16 -0.198 -0.459 -0.198 -0.385 0.222
5 FALSE five 0 -0.122 -0.584 -0.122 -0.145 0.359
6 TRUE five 10 0.0371 -0.354 0.0371 -0.183 0.615
$Europe$d2$`am:carb`
# A tibble: 10 x 8
am.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 6 -0.0166 -0.137 -0.0166 -0.0166 0.104
2 TRUE 1 8 -0.0166 -0.0201 -0.0166 -0.0166 -0.0131
3 FALSE 2 12 -0.0134 -0.216 -0.0134 -0.0163 0.192
4 TRUE 2 7 0.000253 -0.00434 0.000253 0.00193 0.00342
5 FALSE 3 7 -0.0134 -0.134 -0.0134 -0.0163 0.110
6 TRUE 3 2 0.000227 -0.0756 0.000227 -0.00468 0.0802
7 FALSE 4 11 -0.190 -0.229 -0.190 -0.226 -0.121
8 TRUE 4 5 -0.454 -0.542 -0.454 -0.464 -0.359
9 FALSE 8 2 -0.190 -0.228 -0.190 -0.202 -0.143
10 TRUE 8 4 -0.454 -0.620 -0.454 -0.377 -0.355
$Europe$d2$`am:wt`
# A tibble: 20 x 8
am.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 -0.0139 -0.0301 -0.0139 -0.0139 0.00238
2 TRUE 1.50 1 -0.0139 -0.0139 -0.0139 -0.0139 -0.0138
3 FALSE 1.94 0 -0.0139 -0.0398 -0.0139 -0.0139 0.0122
4 TRUE 1.94 7 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
5 FALSE 2.46 1 -0.0138 -0.0502 -0.0138 -0.0139 0.0226
6 TRUE 2.46 6 -0.0138 -0.0340 -0.0138 -0.0138 0.00634
7 FALSE 2.79 1 -0.0314 -0.0517 -0.0314 -0.0426 -0.00165
8 TRUE 2.79 6 -0.0538 -0.0630 -0.0538 -0.0605 -0.0389
9 FALSE 3.19 3 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
10 TRUE 3.19 4 -0.100 -0.104 -0.100 -0.0987 -0.0973
11 FALSE 3.44 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
12 TRUE 3.44 0 -0.109 -0.122 -0.109 -0.105 -0.0990
13 FALSE 3.52 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
14 TRUE 3.52 0 -0.113 -0.125 -0.113 -0.114 -0.0995
15 FALSE 3.73 5 -0.0481 -0.0783 -0.0481 -0.0327 -0.0310
16 TRUE 3.73 2 -0.113 -0.124 -0.113 -0.116 -0.0995
17 FALSE 4.05 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
18 TRUE 4.05 0 -0.113 -0.134 -0.113 -0.105 -0.0990
19 FALSE 5.45 7 -0.0481 -0.0793 -0.0481 -0.0522 -0.0135
20 TRUE 5.45 0 -0.113 -0.134 -0.113 -0.105 -0.0990
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
Code
get(cat_cars_ale, exclude_cols = list(d2_all = "am"))
Output
$Asia
$Asia$d1
$Asia$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 28 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 0.00619 -0.0396 0.00619 0.0297 0.0320
2 TRUE 26 0.0155 -0.00990 0.0155 -0.00670 0.0597
$Asia$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.0226 -0.288 -0.0226 0.0422 0.187
2 Cadillac Fleetwood 2 0.0932 -0.280 0.0932 0.195 0.380
3 Merc 450SL 2 -0.235 -0.766 -0.235 -0.305 0.355
4 Merc 450SLC 2 -0.235 -0.766 -0.235 -0.305 0.355
5 Camaro Z28 2 0.0975 -0.304 0.0975 -0.290 0.829
6 Chrysler Imperial 2 0.264 -0.279 0.264 0.210 0.854
7 Duster 360 2 0.264 -0.279 0.264 0.210 0.854
8 Merc 450SE 2 0.266 -0.279 0.266 0.216 0.854
9 Hornet Sportabout 2 0.433 -0.279 0.433 0.216 1.33
10 AMC Javelin 2 0.446 -0.302 0.446 -0.244 1.78
# i 22 more rows
$Asia$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.118 0.0563 0.118 0.118 0.181
2 four 24 0.00198 -0.150 0.00198 0.0718 0.0948
3 five 10 -0.330 -0.523 -0.330 -0.460 -0.0269
$Asia$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.00549 0.00549 0.00549 0.00549 0.00549
2 2 19 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
3 3 9 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
4 4 16 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
5 8 6 -0.00190 -0.00367 -0.00190 -0.00191 -0.000117
$Asia$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 0.0000319 0.0000319 0.0000319 0.0000319 0.0000319
2 1.94 7 -0.00000143 -0.00000836 -0.00000143 -0.00000427 0.00000790
3 2.46 7 -0.00000906 -0.0000153 -0.00000906 -0.00000787 -0.00000385
4 2.79 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
5 3.19 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
6 3.44 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
7 3.52 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
8 3.73 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
9 4.05 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
10 5.45 7 -0.00000907 -0.0000153 -0.00000907 -0.00000788 -0.00000387
$Asia$d2
$Asia$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 FALSE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 FALSE 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 FALSE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 FALSE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 FALSE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 10.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 14.4 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 15.2 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 16.4 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 18.7 TRUE 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 19.8 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 21.4 TRUE 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 22.9 TRUE 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 30.1 TRUE 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 33.9 TRUE 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 Lincoln Contine~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 Lincoln Contine~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 310 more rows
$Asia$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 three 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 three 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 three 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 three 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 three 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 20 more rows
$Asia$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 40 more rows
$Asia$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 14.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 15.2 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 16.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 18.7 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 19.8 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 21.4 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 22.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 30.1 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 33.9 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 90 more rows
$Asia$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE FALSE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE TRUE 12 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE TRUE 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE Lincoln Continent~ 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE Cadillac Fleetwood 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE Cadillac Fleetwood 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE Merc 450SL 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE Merc 450SL 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE Merc 450SLC 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE Merc 450SLC 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE Camaro Z28 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE Camaro Z28 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
# i 54 more rows
$Asia$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE three 6 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE four 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE four 20 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE five 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE five 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1 14 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 2 10 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 2 9 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 3 8 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 3 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 4 13 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 4 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 8 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 8 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
2 TRUE 1.50 1 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
3 FALSE 1.94 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
4 TRUE 1.94 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
5 FALSE 2.46 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
6 TRUE 2.46 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
7 FALSE 2.79 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
8 TRUE 2.79 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
9 FALSE 3.19 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
10 TRUE 3.19 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
11 FALSE 3.44 2 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
12 TRUE 3.44 5 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
13 FALSE 3.52 3 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
14 TRUE 3.52 4 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
15 FALSE 3.73 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
16 TRUE 3.73 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
17 FALSE 4.05 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
18 TRUE 4.05 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
19 FALSE 5.45 7 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
20 TRUE 5.45 0 1.20e-14 1.20e-14 1.20e-14 1.20e-14 1.20e-14
$Asia$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.0681 -0.184 -0.0681 -0.184 0.146
2 Cadillac Fleetwood three 2 0.0476 -0.184 0.0476 -0.184 0.476
3 Merc 450SL three 2 -0.280 -1.12 -0.280 -0.184 0.476
4 Merc 450SLC three 2 -0.114 -0.643 -0.114 -0.184 0.476
5 Camaro Z28 three 2 0.389 -0.158 0.389 0.342 0.975
6 Chrysler Imperial three 2 0.223 -0.556 0.223 0.274 0.957
7 Duster 360 three 2 0.223 -0.970 0.223 0.272 1.37
8 Merc 450SE three 2 0.224 -0.687 0.224 0.250 1.11
9 Hornet Sportabout three 2 0.225 -0.686 0.225 0.250 1.11
10 AMC Javelin three 2 -0.0923 -1.59 -0.0923 -0.0198 1.34
# i 86 more rows
$Asia$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 3.42 3.42 3.42 3.42 3.42
2 Cadillac Fleetwood 1 0 3.42 3.42 3.42 3.42 3.42
3 Merc 450SL 1 0 3.42 3.42 3.42 3.42 3.42
4 Merc 450SLC 1 0 3.42 3.42 3.42 3.42 3.42
5 Camaro Z28 1 0 3.42 3.42 3.42 3.42 3.42
6 Chrysler Imperial 1 0 3.42 3.00 3.42 3.01 4.19
7 Duster 360 1 0 3.42 -0.295 3.42 2.05 8.30
8 Merc 450SE 1 0 3.42 -3.05 3.42 2.05 11.1
9 Hornet Sportabout 1 0 3.42 -3.45 3.42 2.05 11.5
10 AMC Javelin 1 0 3.42 -3.81 3.42 2.05 11.8
# i 150 more rows
$Asia$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.342 0.342 0.342 0.342 0.342
2 Cadillac Fleetwood 1.50 0 0.342 0.342 0.342 0.342 0.342
3 Merc 450SL 1.50 0 0.342 0.342 0.342 0.342 0.342
4 Merc 450SLC 1.50 0 0.342 0.342 0.342 0.342 0.342
5 Camaro Z28 1.50 0 0.342 0.342 0.342 0.342 0.342
6 Chrysler Imperial 1.50 0 0.342 0.319 0.342 0.342 0.366
7 Duster 360 1.50 0 0.342 0.284 0.342 0.286 0.448
8 Merc 450SE 1.50 0 0.342 -0.0164 0.342 0.286 0.749
9 Hornet Sportabout 1.50 0 0.342 -0.0274 0.342 0.286 0.760
10 AMC Javelin 1.50 0 0.342 -0.0351 0.342 0.286 0.768
# i 310 more rows
$Asia$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.0284 0.0284 0.0284 0.0284 0.0284
2 four 1 8 0.0284 -0.0188 0.0284 0.0249 0.0785
3 five 1 0 0.0284 -0.0220 0.0284 0.0249 0.0817
4 three 2 8 0.0270 -0.0718 0.0270 0.0206 0.131
5 four 2 7 0.118 0.0612 0.118 0.135 0.161
6 five 2 4 0.0970 0.00282 0.0970 0.130 0.163
7 three 3 7 0.0341 -0.0182 0.0341 0.0372 0.0838
8 four 3 2 0.149 0.120 0.149 0.140 0.185
9 five 3 0 0.123 0.0471 0.123 0.139 0.187
10 three 4 8 0.0456 -0.0699 0.0456 0.0598 0.149
11 four 4 6 0.307 0.206 0.307 0.358 0.364
12 five 4 2 0.285 0.221 0.285 0.270 0.361
13 three 8 1 0.0456 -0.0204 0.0456 0.0574 0.102
14 four 8 1 0.308 0.256 0.308 0.307 0.359
15 five 8 4 0.285 0.217 0.285 0.273 0.364
$Asia$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 0.0363 0.0363 0.0363 0.0363 0.0363
2 four 1.50 0 0.0363 0.0274 0.0363 0.0364 0.0453
3 five 1.50 1 0.0363 0.0212 0.0363 0.0389 0.0493
4 three 1.94 0 0.0470 0.0108 0.0470 0.0619 0.0704
5 four 1.94 6 0.0550 0.00593 0.0550 0.0757 0.0864
6 five 1.94 1 0.0552 0.00661 0.0552 0.0757 0.0864
7 three 2.46 1 0.0487 -0.00963 0.0487 0.0762 0.0838
8 four 2.46 4 0.0623 0.0132 0.0623 0.0852 0.0919
9 five 2.46 2 0.0725 0.0313 0.0725 0.0923 0.0970
10 three 2.79 1 0.0489 -0.00962 0.0489 0.0764 0.0840
# i 20 more rows
$Asia$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.00454 0.00454 0.00454 0.00454 0.00454
2 2 1.50 1 0.00454 -0.0270 0.00454 0.00454 0.0361
3 3 1.50 0 0.00454 -0.0216 0.00454 0.00454 0.0307
4 4 1.50 0 0.00454 -0.0310 0.00454 0.00454 0.0401
5 8 1.50 0 0.00454 -0.0316 0.00454 0.00454 0.0407
6 1 1.94 4 0.00454 -0.00336 0.00454 0.00454 0.0124
7 2 1.94 3 0.0185 -0.0159 0.0185 0.00668 0.0629
8 3 1.94 0 0.0185 -0.00651 0.0185 0.00668 0.0535
9 4 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
10 8 1.94 0 0.0185 -0.0159 0.0185 0.00668 0.0629
# i 40 more rows
$Europe
$Europe$d1
$Europe$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 28 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -0.00619 -0.0320 -0.00619 -0.0297 0.0396
2 TRUE 26 -0.0155 -0.0597 -0.0155 0.00670 0.00990
$Europe$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 0.217 -0.164 0.217 0.166 0.641
2 Cadillac Fleetwood 2 0.268 -0.494 0.268 0.166 1.12
3 Merc 450SL 2 0.931 -0.444 0.931 1.17 2.10
4 Merc 450SLC 2 0.764 -0.444 0.764 1.17 1.63
5 Camaro Z28 2 -0.0688 -1.44 -0.0688 0.166 1.10
6 Chrysler Imperial 2 0.0978 -0.967 0.0978 0.166 1.10
7 Duster 360 2 0.0978 -0.967 0.0978 0.166 1.10
8 Merc 450SE 2 0.596 -0.917 0.596 1.17 1.62
9 Hornet Sportabout 2 -0.237 -1.92 -0.237 0.166 1.10
10 AMC Javelin 2 -0.0841 -1.92 -0.0841 0.166 1.54
# i 22 more rows
$Europe$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 -0.239 -0.366 -0.239 -0.239 -0.112
2 four 24 0.247 0.140 0.247 0.211 0.385
3 five 10 0.380 -0.0309 0.380 0.594 0.609
$Europe$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 -0.00549 -0.00549 -0.00549 -0.00549 -0.00549
2 2 19 0.00190 0.000117 0.00190 0.00191 0.00367
3 3 9 0.00190 0.000117 0.00190 0.00191 0.00367
4 4 16 0.00190 0.000117 0.00190 0.00191 0.00367
5 8 6 0.00190 0.000117 0.00190 0.00191 0.00367
$Europe$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 -0.0000319 -0.0000319 -0.0000319 -0.0000319 -0.0000319
2 1.94 7 0.00000143 -0.00000790 0.00000143 0.00000427 0.00000836
3 2.46 7 0.00000906 0.00000385 0.00000906 0.00000787 0.0000153
4 2.79 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
5 3.19 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
6 3.44 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
7 3.52 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
8 3.73 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
9 4.05 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
10 5.45 7 0.00000907 0.00000387 0.00000907 0.00000788 0.0000153
$Europe$d2
$Europe$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 FALSE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 FALSE 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 FALSE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 FALSE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 FALSE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 10.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 14.4 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 15.2 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 16.4 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 18.7 TRUE 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 19.8 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 21.4 TRUE 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 22.9 TRUE 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 30.1 TRUE 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 33.9 TRUE 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 Lincoln Contine~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 Lincoln Contine~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 310 more rows
$Europe$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 three 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 three 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 three 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 three 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 three 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 20 more rows
$Europe$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 40 more rows
$Europe$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 14.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 15.2 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 16.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 18.7 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 19.8 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 21.4 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 22.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 30.1 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 33.9 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 90 more rows
$Europe$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE FALSE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE TRUE 12 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE TRUE 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE Lincoln Continent~ 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE Cadillac Fleetwood 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE Cadillac Fleetwood 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE Merc 450SL 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE Merc 450SL 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE Merc 450SLC 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE Merc 450SLC 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE Camaro Z28 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE Camaro Z28 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
# i 54 more rows
$Europe$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE three 6 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE four 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE four 20 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE five 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE five 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1 14 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 2 10 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 2 9 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 3 8 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 3 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 4 13 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 4 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 8 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 8 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
2 TRUE 1.50 1 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
3 FALSE 1.94 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
4 TRUE 1.94 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
5 FALSE 2.46 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
6 TRUE 2.46 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
7 FALSE 2.79 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
8 TRUE 2.79 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
9 FALSE 3.19 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
10 TRUE 3.19 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
11 FALSE 3.44 2 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
12 TRUE 3.44 5 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
13 FALSE 3.52 3 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
14 TRUE 3.52 4 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
15 FALSE 3.73 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
16 TRUE 3.73 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
17 FALSE 4.05 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
18 TRUE 4.05 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
19 FALSE 5.45 7 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
20 TRUE 5.45 0 1.31e-14 1.31e-14 1.31e-14 1.31e-14 1.31e-14
$Europe$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 0.185 -0.0288 0.185 0.301 0.301
2 Cadillac Fleetwood three 2 0.0696 -0.359 0.0696 0.301 0.301
3 Merc 450SL three 2 0.399 -0.359 0.399 0.301 1.24
4 Merc 450SLC three 2 0.399 -0.359 0.399 0.301 1.24
5 Camaro Z28 three 2 0.0663 -0.832 0.0663 0.301 0.765
6 Chrysler Imperial three 2 0.0663 -0.450 0.0663 -0.0912 0.717
7 Duster 360 three 2 0.0663 -0.693 0.0663 0.211 0.703
8 Merc 450SE three 2 0.0643 -0.692 0.0643 0.235 0.676
9 Hornet Sportabout three 2 0.0643 -0.533 0.0643 -0.0418 0.752
10 AMC Javelin three 2 0.218 -0.455 0.218 0.177 0.924
# i 86 more rows
$Europe$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
2 Cadillac Fleetwood 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
3 Merc 450SL 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
4 Merc 450SLC 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
5 Camaro Z28 1 0 -3.89 -3.89 -3.89 -3.89 -3.89
6 Chrysler Imperial 1 0 -3.89 -5.43 -3.89 -5.05 -1.37
7 Duster 360 1 0 -3.89 -8.50 -3.89 -5.26 1.88
8 Merc 450SE 1 0 -3.89 -10.8 -3.89 -5.26 4.14
9 Hornet Sportabout 1 0 -3.89 -10.8 -3.89 -5.26 4.15
10 AMC Javelin 1 0 -3.89 -11.1 -3.89 -5.26 4.50
# i 150 more rows
$Europe$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
2 Cadillac Fleetwood 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
3 Merc 450SL 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
4 Merc 450SLC 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
5 Camaro Z28 1.50 0 -0.403 -0.403 -0.403 -0.403 -0.403
6 Chrysler Imperial 1.50 0 -0.403 -0.460 -0.403 -0.459 -0.297
7 Duster 360 1.50 0 -0.403 -0.550 -0.403 -0.459 -0.208
8 Merc 450SE 1.50 0 -0.403 -0.762 -0.403 -0.459 0.00423
9 Hornet Sportabout 1.50 0 -0.403 -0.773 -0.403 -0.459 0.0150
10 AMC Javelin 1.50 0 -0.403 -0.780 -0.403 -0.459 0.0226
# i 310 more rows
$Europe$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 0.00218 -0.0999 0.00218 0.00214 0.104
2 four 1 8 0.00218 -0.147 0.00218 -0.00135 0.154
3 five 1 0 0.00218 -0.150 0.00218 -0.00135 0.158
4 three 2 8 0.00616 -0.199 0.00616 0.0106 0.208
5 four 2 7 0.0104 -0.198 0.0104 0.0250 0.207
6 five 2 4 0.0315 -0.0678 0.0315 0.0249 0.137
7 three 3 7 0.00616 -0.147 0.00616 0.0106 0.155
8 four 3 2 0.0104 -0.149 0.0104 0.0227 0.159
9 five 3 0 0.0369 -0.152 0.0369 0.0214 0.239
10 three 4 8 0.00616 -0.199 0.00616 0.0129 0.206
11 four 4 6 0.0104 -0.0985 0.0104 0.0262 0.106
12 five 4 2 0.0369 -0.0995 0.0369 0.0214 0.187
13 three 8 1 0.00616 -0.150 0.00616 0.0106 0.158
14 four 8 1 0.00933 -0.201 0.00933 0.0227 0.209
15 five 8 4 0.0359 -0.155 0.0359 0.0236 0.237
$Europe$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.00213 -0.0403 -0.00213 -0.00217 0.0361
2 four 1.50 0 -0.00213 -0.0492 -0.00213 -0.00213 0.0450
3 five 1.50 1 -0.00213 -0.0555 -0.00213 0.000424 0.0490
4 three 1.94 0 -0.00212 -0.0669 -0.00212 0.000451 0.0605
5 four 1.94 6 -0.00207 -0.0881 -0.00207 0.000513 0.0818
6 five 1.94 1 -0.00231 -0.0888 -0.00231 0.000513 0.0818
7 three 2.46 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
8 four 2.46 4 -0.00205 -0.0881 -0.00205 0.000523 0.0818
9 five 2.46 2 -0.0123 -0.106 -0.0123 -0.0115 0.0812
10 three 2.79 1 -0.00212 -0.0881 -0.00212 0.000462 0.0817
# i 20 more rows
$Europe$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 -0.00454 -0.0267 -0.00454 -0.00454 0.0176
2 2 1.50 1 -0.00454 -0.0361 -0.00454 -0.00454 0.0270
3 3 1.50 0 -0.00454 -0.0401 -0.00454 -0.00454 0.0310
4 4 1.50 0 -0.00454 -0.00916 -0.00454 -0.00454 0.0000810
5 8 1.50 0 -0.00454 -0.0407 -0.00454 -0.00454 0.0316
6 1 1.94 4 -0.00454 -0.0346 -0.00454 -0.00454 0.0255
7 2 1.94 3 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
8 3 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
9 4 1.94 0 -0.0185 -0.0319 -0.0185 -0.0161 -0.00715
10 8 1.94 0 -0.0185 -0.0629 -0.0185 -0.00668 0.0159
# i 40 more rows
$`North America`
$`North America`$d1
$`North America`$d1$mpg
# A tibble: 10 x 7
mpg.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$vs
# A tibble: 2 x 7
vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 36 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 28 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d1$am
# A tibble: 2 x 7
am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 38 -5.57e-10 -9.65e-10 -5.57e-10 -8.52e-10 1.02e-10
2 TRUE 26 1.57e-10 -2.14e-10 1.57e-10 -4.71e-11 7.00e-10
$`North America`$d1$model
# A tibble: 32 x 7
model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 2 -0.195 -0.811 -0.195 0.139 0.139
2 Cadillac Fleetwood 2 -0.361 -1.29 -0.361 0.139 0.139
3 Merc 450SL 2 -0.695 -1.34 -0.695 -0.861 0.0888
4 Merc 450SLC 2 -0.529 -0.863 -0.529 -0.861 0.0888
5 Camaro Z28 2 -0.0287 -0.813 -0.0287 0.139 0.614
6 Chrysler Imperial 2 -0.362 -1.29 -0.362 0.139 0.139
7 Duster 360 2 -0.362 -1.29 -0.362 0.139 0.139
8 Merc 450SE 2 -0.862 -1.81 -0.862 -0.861 0.0888
9 Hornet Sportabout 2 -0.195 -1.29 -0.195 0.139 0.614
10 AMC Javelin 2 -0.362 -1.29 -0.362 0.139 0.139
# i 22 more rows
$`North America`$d1$gear
# A tibble: 3 x 7
gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 30 0.120 0.0527 0.120 0.0544 0.244
2 four 24 -0.249 -0.280 -0.249 -0.232 -0.232
3 five 10 -0.0498 -0.131 -0.0498 -0.0843 0.0603
$`North America`$d1$carb
# A tibble: 5 x 7
carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 14 0.000000108 0.000000108 0.000000108 1.08e-7 1.08e- 7
2 2 19 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.22e-10
3 3 9 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
4 4 16 -0.0000000359 -0.0000000704 -0.0000000359 -3.71e-8 -5.24e-10
5 8 6 -0.0000000413 -0.0000000817 -0.0000000413 -4.13e-8 -9.49e-10
$`North America`$d1$wt
# A tibble: 10 x 7
wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.50 1 6.60e-10 6.60e-10 6.60e-10 6.60e-10 6.60e-10
2 1.94 7 2.64e-10 -1.47e-12 2.64e-10 1.40e-10 6.34e-10
3 2.46 7 2.19e-10 -4.35e-11 2.19e-10 6.88e-11 6.08e-10
4 2.79 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
5 3.19 7 2.19e-10 -4.36e-11 2.19e-10 6.88e-11 6.08e-10
6 3.44 7 2.19e-10 -4.28e-11 2.19e-10 6.88e-11 6.09e-10
7 3.52 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
8 3.73 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
9 4.05 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
10 5.45 7 2.19e-10 -4.28e-11 2.19e-10 6.90e-11 6.10e-10
$`North America`$d2
$`North America`$d2$`mpg:vs`
# A tibble: 20 x 8
mpg.ceil vs.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:am`
# A tibble: 20 x 8
mpg.ceil am.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 FALSE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 FALSE 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 FALSE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 FALSE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 FALSE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 10.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 14.4 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 15.2 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 16.4 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 18.7 TRUE 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 19.8 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 21.4 TRUE 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 22.9 TRUE 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 30.1 TRUE 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 33.9 TRUE 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`mpg:model`
# A tibble: 320 x 8
mpg.ceil model.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 Lincoln Contine~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 Lincoln Contine~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 310 more rows
$`North America`$d2$`mpg:gear`
# A tibble: 30 x 8
mpg.ceil gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 three 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 three 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 three 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 three 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 three 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 20 more rows
$`North America`$d2$`mpg:carb`
# A tibble: 50 x 8
mpg.ceil carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 40 more rows
$`North America`$d2$`mpg:wt`
# A tibble: 100 x 8
mpg.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 14.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 15.2 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 16.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 18.7 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 19.8 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 21.4 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 22.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 30.1 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 33.9 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 90 more rows
$`North America`$d2$`vs:am`
# A tibble: 4 x 8
vs.bin am.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE FALSE 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE FALSE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE TRUE 12 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE TRUE 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:model`
# A tibble: 64 x 8
vs.bin model.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE Lincoln Continent~ 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE Lincoln Continent~ 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE Cadillac Fleetwood 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE Cadillac Fleetwood 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE Merc 450SL 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE Merc 450SL 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE Merc 450SLC 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE Merc 450SLC 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE Camaro Z28 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE Camaro Z28 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
# i 54 more rows
$`North America`$d2$`vs:gear`
# A tibble: 6 x 8
vs.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE three 24 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE three 6 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE four 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE four 20 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE five 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE five 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:carb`
# A tibble: 10 x 8
vs.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1 14 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 2 10 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 2 9 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 3 8 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 3 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 4 13 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 4 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 8 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 8 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`vs:wt`
# A tibble: 20 x 8
vs.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE 1.50 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
2 TRUE 1.50 1 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
3 FALSE 1.94 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
4 TRUE 1.94 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
5 FALSE 2.46 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
6 TRUE 2.46 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
7 FALSE 2.79 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
8 TRUE 2.79 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
9 FALSE 3.19 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
10 TRUE 3.19 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
11 FALSE 3.44 2 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
12 TRUE 3.44 5 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
13 FALSE 3.52 3 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
14 TRUE 3.52 4 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
15 FALSE 3.73 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
16 TRUE 3.73 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
17 FALSE 4.05 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
18 TRUE 4.05 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
19 FALSE 5.45 7 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
20 TRUE 5.45 0 2.64e-14 2.64e-14 2.64e-14 2.64e-14 2.64e-14
$`North America`$d2$`model:gear`
# A tibble: 96 x 8
model.bin gear.bin .n .y .y_lo .y_mean .y_median .y_hi
<ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental three 2 -0.117 -0.117 -0.117 -0.117 -0.117
2 Cadillac Fleetwood three 2 -0.117 -0.117 -0.117 -0.117 -0.117
3 Merc 450SL three 2 -0.119 -0.122 -0.119 -0.117 -0.117
4 Merc 450SLC three 2 -0.285 -0.596 -0.285 -0.117 -0.117
5 Camaro Z28 three 2 -0.455 -1.08 -0.455 -0.117 -0.117
6 Chrysler Imperial three 2 -0.289 -1.42 -0.289 0.305 0.337
7 Duster 360 three 2 -0.289 -1.15 -0.289 0.0335 0.303
8 Merc 450SE three 2 -0.289 -1.15 -0.289 0.0334 0.303
9 Hornet Sportabout three 2 -0.289 -1.59 -0.289 0.398 0.432
10 AMC Javelin three 2 -0.125 -1.13 -0.125 0.285 0.534
# i 86 more rows
$`North America`$d2$`model:carb`
# A tibble: 160 x 8
model.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1 0 0.471 0.471 0.471 0.471 0.471
2 Cadillac Fleetwood 1 0 0.471 0.471 0.471 0.471 0.471
3 Merc 450SL 1 0 0.471 0.471 0.471 0.471 0.471
4 Merc 450SLC 1 0 0.471 0.471 0.471 0.471 0.471
5 Camaro Z28 1 0 0.471 0.471 0.471 0.471 0.471
6 Chrysler Imperial 1 0 0.471 -1.49 0.471 -0.690 3.42
7 Duster 360 1 0 0.471 -5.32 0.471 -0.895 7.42
8 Merc 450SE 1 0 0.471 -6.40 0.471 -0.895 8.51
9 Hornet Sportabout 1 0 0.471 -6.67 0.471 -0.895 8.77
10 AMC Javelin 1 0 0.471 -6.76 0.471 -0.895 8.87
# i 150 more rows
$`North America`$d2$`model:wt`
# A tibble: 320 x 8
model.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lincoln Continental 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
2 Cadillac Fleetwood 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
3 Merc 450SL 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
4 Merc 450SLC 1.50 0 0.0603 0.0603 0.0603 0.0603 0.0603
5 Camaro Z28 1.50 0 0.0603 0.0527 0.0603 0.0602 0.0681
6 Chrysler Imperial 1.50 0 0.0603 0.00257 0.0603 0.00402 0.166
7 Duster 360 1.50 0 0.0603 -0.239 0.0603 0.00402 0.407
8 Merc 450SE 1.50 0 0.0603 -0.301 0.0603 0.00402 0.470
9 Hornet Sportabout 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
10 AMC Javelin 1.50 0 0.0603 -0.317 0.0603 0.00402 0.486
# i 310 more rows
$`North America`$d2$`gear:carb`
# A tibble: 15 x 8
gear.bin carb.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1 6 -0.0306 -0.106 -0.0306 -0.0663 0.0748
2 four 1 8 -0.0306 -0.153 -0.0306 -0.0698 0.125
3 five 1 0 -0.0306 -0.156 -0.0306 -0.0698 0.128
4 three 2 8 -0.0331 -0.206 -0.0331 -0.0775 0.177
5 four 2 7 -0.129 -0.341 -0.129 -0.227 0.167
6 five 2 4 -0.129 -0.241 -0.129 -0.226 0.0664
7 three 3 7 -0.0403 -0.154 -0.0403 -0.0940 0.119
8 four 3 2 -0.159 -0.322 -0.159 -0.257 0.0858
9 five 3 0 -0.160 -0.374 -0.160 -0.257 0.135
10 three 4 8 -0.0518 -0.208 -0.0518 -0.112 0.155
11 four 4 6 -0.317 -0.437 -0.317 -0.427 -0.105
12 five 4 2 -0.322 -0.494 -0.322 -0.433 -0.0547
13 three 8 1 -0.0518 -0.158 -0.0518 -0.114 0.108
14 four 8 1 -0.317 -0.534 -0.317 -0.431 -0.00255
15 five 8 4 -0.321 -0.544 -0.321 -0.432 -0.00476
$`North America`$d2$`gear:wt`
# A tibble: 30 x 8
gear.bin wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<ord> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 three 1.50 0 -0.0342 -0.0869 -0.0342 -0.0210 0.00727
2 four 1.50 0 -0.0342 -0.0958 -0.0342 -0.0210 0.0162
3 five 1.50 1 -0.0342 -0.102 -0.0342 -0.0184 0.0202
4 three 1.94 0 -0.0448 -0.115 -0.0448 -0.0504 0.0301
5 four 1.94 6 -0.0529 -0.152 -0.0529 -0.0553 0.0484
6 five 1.94 1 -0.0529 -0.152 -0.0529 -0.0553 0.0484
7 three 2.46 1 -0.0466 -0.137 -0.0466 -0.0557 0.0510
8 four 2.46 4 -0.0603 -0.159 -0.0603 -0.0648 0.0428
9 five 2.46 2 -0.0603 -0.159 -0.0603 -0.0648 0.0428
10 three 2.79 1 -0.0468 -0.137 -0.0468 -0.0559 0.0509
# i 20 more rows
$`North America`$d2$`carb:wt`
# A tibble: 50 x 8
carb.ceil wt.ceil .n .y .y_lo .y_mean .y_median .y_hi
<dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.50 0 0.0000000171 -0.0315 0.0000000171 -1.68e-7 0.0315
2 2 1.50 1 0.0000000171 -0.00404 0.0000000171 -1.03e-8 0.00404
3 3 1.50 0 0.0000000171 -0.0356 0.0000000171 -1.96e-7 0.0356
4 4 1.50 0 0.0000000171 -0.0267 0.0000000171 -1.34e-7 0.0267
5 8 1.50 0 0.0000000171 -0.0362 0.0000000171 -1.95e-7 0.0362
6 1 1.94 4 0.0000000171 -0.0394 0.0000000171 -1.98e-7 0.0394
7 2 1.94 3 0.000000105 -0.00790 0.000000105 -5.22e-9 0.00790
8 3 1.94 0 0.000000105 -0.0394 0.000000105 -1.91e-7 0.0394
9 4 1.94 0 0.000000105 -0.0300 0.000000105 -1.26e-7 0.0300
10 8 1.94 0 0.000000105 -0.0394 0.000000105 -1.88e-7 0.0394
# i 40 more rows
Code
get(cat_cars_ale, stats = "conf_regions")
Message
! Note that confidence regions are not reliable with fewer than 100 bootstrap iterations or p-values based on fewer than 100 random iterations.
i There are 2 bootstrap iterations.
i p-values are based on 100 iterations.
Output
$Asia
$Asia$d1
# A tibble: 45 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 1.20e-14
2 vs FALSE NA NA NA 36 56.2 1.20e-14 NA
3 vs TRUE NA NA NA 28 43.8 1.20e-14 NA
4 am FALSE NA NA NA 38 59.4 6.19e- 3 NA
5 am TRUE NA NA NA 26 40.6 1.55e- 2 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 -2.26e- 2 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 9.32e- 2 NA
8 model Merc 450SL NA NA NA 2 3.12 -2.35e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 -2.35e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 9.75e- 2 NA
# i 35 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$Asia$d2
# A tibble: 315 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 1.20e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 1.20e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 1.20e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 1.20e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 1.20e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 1.20e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 1.20e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 1.20e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 1.20e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 1.20e-14
# i 305 more rows
$Europe
$Europe$d1
# A tibble: 45 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 1.31e-14
2 vs FALSE NA NA NA 36 56.2 1.31e-14 NA
3 vs TRUE NA NA NA 28 43.8 1.31e-14 NA
4 am FALSE NA NA NA 38 59.4 -6.19e- 3 NA
5 am TRUE NA NA NA 26 40.6 -1.55e- 2 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 2.17e- 1 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 2.68e- 1 NA
8 model Merc 450SL NA NA NA 2 3.12 9.31e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 7.64e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 -6.88e- 2 NA
# i 35 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$Europe$d2
# A tibble: 310 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 1.31e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 1.31e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 1.31e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 1.31e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 1.31e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 1.31e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 1.31e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 1.31e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 1.31e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 1.31e-14
# i 300 more rows
$`North America`
$`North America`$d1
# A tibble: 44 x 12
term x start_x end_x x_span_pct n pct y start_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 mpg <NA> 10.4 33.9 100 64 100 NA 2.64e-14
2 vs FALSE NA NA NA 36 56.2 2.64e-14 NA
3 vs TRUE NA NA NA 28 43.8 2.64e-14 NA
4 am FALSE NA NA NA 38 59.4 -5.57e-10 NA
5 am TRUE NA NA NA 26 40.6 1.57e-10 NA
6 model Lincoln Cont~ NA NA NA 2 3.12 -1.95e- 1 NA
7 model Cadillac Fle~ NA NA NA 2 3.12 -3.61e- 1 NA
8 model Merc 450SL NA NA NA 2 3.12 -6.95e- 1 NA
9 model Merc 450SLC NA NA NA 2 3.12 -5.29e- 1 NA
10 model Camaro Z28 NA NA NA 2 3.12 -2.87e- 2 NA
# i 34 more rows
# i 3 more variables: end_y <dbl>, trend <dbl>, aler_band <ord>
$`North America`$d2
# A tibble: 307 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 mpg [10.4,18.7] vs FALSE overlap 25 39.1 2.64e-14
2 mpg (18.7,21.4] vs FALSE overlap 9 14.1 2.64e-14
3 mpg (21.4,33.9] vs FALSE overlap 2 3.12 2.64e-14
4 mpg [10.4,18.7] vs TRUE overlap 4 6.25 2.64e-14
5 mpg (18.7,21.4] vs TRUE overlap 5 7.81 2.64e-14
6 mpg (21.4,33.9] vs TRUE overlap 19 29.7 2.64e-14
7 mpg [10.4,16.4] am FALSE overlap 18 28.1 2.64e-14
8 mpg (16.4,21.4] am FALSE overlap 13 20.3 2.64e-14
9 mpg (21.4,33.9] am FALSE overlap 7 10.9 2.64e-14
10 mpg [10.4,16.4] am TRUE overlap 4 6.25 2.64e-14
# i 297 more rows
Code
get(cat_cars_ale, stats = "conf_sig")
Message
! Note that confidence regions are not reliable with fewer than 100 bootstrap iterations or p-values based on fewer than 100 random iterations.
i There are 2 bootstrap iterations.
i p-values are based on 100 iterations.
Output
$Asia
$Asia$d1
# A tibble: 8 x 12
term x start_x end_x x_span_pct n pct y start_y end_y
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 gear three NA NA NA 30 46.9 0.118 NA NA
2 gear four NA NA NA 24 37.5 0.00198 NA NA
3 gear five NA NA NA 10 15.6 -0.330 NA NA
4 carb <NA> 1 1 0 14 21.9 NA 0.00549 5.49e-3
5 carb <NA> 2 8 85.7 50 78.1 NA -0.00190 -1.90e-3
6 wt <NA> 1.50 1.50 0 1 1.56 NA 0.0000319 3.19e-5
7 wt <NA> 1.94 1.94 0 7 10.9 NA -0.00000143 -1.43e-6
8 wt <NA> 2.46 5.45 75.6 56 87.5 NA -0.00000906 -9.07e-6
# i 2 more variables: trend <dbl>, aler_band <ord>
$Asia$d2
# A tibble: 128 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE gear three below 30 46.9 -0.0927
2 am FALSE gear four below 8 12.5 -0.191
3 am TRUE gear four overlap 16 25 -0.433
4 am TRUE gear five below 10 15.6 -0.766
5 am FALSE carb [1,2] above 6 9.38 0.0166
6 am TRUE carb [1,2] overlap 15 23.4 0.00816
7 am FALSE carb [1,2] overlap 12 18.8 0.0134
8 am FALSE carb (2,4] above 7 10.9 0.0134
9 am TRUE carb (2,4] overlap 2 3.12 -0.000226
10 am FALSE carb (2,4] overlap 11 17.2 0.190
# i 118 more rows
$Europe
$Europe$d1
# A tibble: 40 x 12
term x start_x end_x x_span_pct n pct y start_y end_y trend
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 model Linco~ NA NA NA 2 3.12 0.217 NA NA NA
2 model Cadil~ NA NA NA 2 3.12 0.268 NA NA NA
3 model Merc ~ NA NA NA 2 3.12 0.931 NA NA NA
4 model Merc ~ NA NA NA 2 3.12 0.764 NA NA NA
5 model Camar~ NA NA NA 2 3.12 -0.0688 NA NA NA
6 model Chrys~ NA NA NA 2 3.12 0.0978 NA NA NA
7 model Duste~ NA NA NA 2 3.12 0.0978 NA NA NA
8 model Merc ~ NA NA NA 2 3.12 0.596 NA NA NA
9 model Horne~ NA NA NA 2 3.12 -0.237 NA NA NA
10 model AMC J~ NA NA NA 2 3.12 -0.0841 NA NA NA
# i 30 more rows
# i 1 more variable: aler_band <ord>
$Europe$d2
# A tibble: 133 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE carb [1,2] overlap 18 28.1 -0.0150
2 am TRUE carb [1,2] below 8 12.5 -0.0166
3 am TRUE carb [1,2] overlap 7 10.9 0.000253
4 am FALSE carb (2,4] overlap 7 10.9 -0.0134
5 am TRUE carb (2,4] overlap 2 3.12 0.000227
6 am FALSE carb (2,4] below 11 17.2 -0.190
7 am TRUE carb (2,4] below 5 7.81 -0.454
8 am FALSE carb (4,8] below 2 3.12 -0.190
9 am TRUE carb (4,8] below 4 6.25 -0.454
10 am TRUE wt [1.5,2.79] below 7 10.9 -0.0338
# i 123 more rows
$`North America`
$`North America`$d1
# A tibble: 37 x 12
term x start_x end_x x_span_pct n pct y start_y end_y trend
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 model Linco~ NA NA NA 2 3.12 -0.195 NA NA NA
2 model Cadil~ NA NA NA 2 3.12 -0.361 NA NA NA
3 model Merc ~ NA NA NA 2 3.12 -0.695 NA NA NA
4 model Merc ~ NA NA NA 2 3.12 -0.529 NA NA NA
5 model Camar~ NA NA NA 2 3.12 -0.0287 NA NA NA
6 model Chrys~ NA NA NA 2 3.12 -0.362 NA NA NA
7 model Duste~ NA NA NA 2 3.12 -0.362 NA NA NA
8 model Merc ~ NA NA NA 2 3.12 -0.862 NA NA NA
9 model Horne~ NA NA NA 2 3.12 -0.195 NA NA NA
10 model AMC J~ NA NA NA 2 3.12 -0.362 NA NA NA
# i 27 more rows
# i 1 more variable: aler_band <ord>
$`North America`$d2
# A tibble: 167 x 8
term1 x1 term2 x2 aler_band n pct y
<chr> <chr> <chr> <chr> <ord> <int> <dbl> <dbl>
1 am FALSE model Lincoln Continental below 2 3.12 -0.0218
2 am FALSE model Cadillac Fleetwood below 2 3.12 -0.0218
3 am FALSE model Merc 450SL below 2 3.12 -0.0226
4 am FALSE model Merc 450SLC below 2 3.12 -0.0226
5 am FALSE model Camaro Z28 below 2 3.12 -0.0226
6 am FALSE model Chrysler Imperial overlap 2 3.12 -0.0226
7 am FALSE model Duster 360 overlap 2 3.12 -0.0226
8 am FALSE model Merc 450SE overlap 2 3.12 -0.0230
9 am FALSE model Hornet Sportabout overlap 2 3.12 -0.0230
10 am FALSE model AMC Javelin overlap 2 3.12 -0.0230
# i 157 more rows
Code
ale_plots_to_data(cat_cars_ale_plots)
Condition
Warning:
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i Did you mean to specify a different guide `position`?
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i Did you mean to specify a different guide `position`?
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i Did you mean to specify a different guide `position`?
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Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
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i Did you mean to specify a different guide `position`?
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Position guide is perpendicular to the intended axis.
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Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
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Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
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Position guide is perpendicular to the intended axis.
i Did you mean to specify a different guide `position`?
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Output
$Asia
$Asia$d1
$Asia$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.197411e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 1.19744e-14
2 -Inf Inf 1.197341e-14 1.19744e-14
3 -Inf Inf 1.197341e-14 1.19744e-14
4 -Inf Inf 1.197341e-14 1.19744e-14
5 -Inf Inf 1.197341e-14 1.19744e-14
6 -Inf Inf 1.197341e-14 1.19744e-14
7 -Inf Inf 1.197341e-14 1.19744e-14
8 -Inf Inf 1.197341e-14 1.19744e-14
9 -Inf Inf 1.197341e-14 1.19744e-14
10 -Inf Inf 1.197341e-14 1.19744e-14
$Asia$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.197411e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.197411e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
$Asia$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 0.006188871 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 0.015482442 1 2 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.197341e-14 1.19744e-14
2 1.197341e-14 1.19744e-14
$Asia$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.02256553 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.09317291 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.23524785 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.23524785 1 4 NA lightgray 0.5 1 NA -Inf
5 5 0.09751143 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.26417810 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.26417810 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.26611036 1 8 NA lightgray 0.5 1 NA -Inf
9 9 0.43273667 1 9 NA lightgray 0.5 1 NA -Inf
10 10 0.44616466 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.44616466 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.44616466 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.44616466 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.77949799 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.22030910 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.05364243 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -0.05370066 1 17 NA lightgray 0.5 1 NA -Inf
18 18 0.11220276 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.11220281 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.14577267 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -0.02754194 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -0.36770131 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -0.03661493 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -0.69864333 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -0.69864332 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -0.69864353 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.02590338 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -0.52590572 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.02589030 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -0.85922071 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -0.52591213 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.02590860 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
4 Inf 1.197341e-14 1.19744e-14
5 Inf 1.197341e-14 1.19744e-14
6 Inf 1.197341e-14 1.19744e-14
7 Inf 1.197341e-14 1.19744e-14
8 Inf 1.197341e-14 1.19744e-14
9 Inf 1.197341e-14 1.19744e-14
10 Inf 1.197341e-14 1.19744e-14
11 Inf 1.197341e-14 1.19744e-14
12 Inf 1.197341e-14 1.19744e-14
13 Inf 1.197341e-14 1.19744e-14
14 Inf 1.197341e-14 1.19744e-14
15 Inf 1.197341e-14 1.19744e-14
16 Inf 1.197341e-14 1.19744e-14
17 Inf 1.197341e-14 1.19744e-14
18 Inf 1.197341e-14 1.19744e-14
19 Inf 1.197341e-14 1.19744e-14
20 Inf 1.197341e-14 1.19744e-14
21 Inf 1.197341e-14 1.19744e-14
22 Inf 1.197341e-14 1.19744e-14
23 Inf 1.197341e-14 1.19744e-14
24 Inf 1.197341e-14 1.19744e-14
25 Inf 1.197341e-14 1.19744e-14
26 Inf 1.197341e-14 1.19744e-14
27 Inf 1.197341e-14 1.19744e-14
28 Inf 1.197341e-14 1.19744e-14
29 Inf 1.197341e-14 1.19744e-14
30 Inf 1.197341e-14 1.19744e-14
31 Inf 1.197341e-14 1.19744e-14
32 Inf 1.197341e-14 1.19744e-14
$Asia$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.330118650 1 3 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
$Asia$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.005489234 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -0.001898356 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 -0.001898365 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 1.19744e-14
2 Inf 1.197341e-14 1.19744e-14
3 Inf 1.197341e-14 1.19744e-14
4 Inf 1.197341e-14 1.19744e-14
5 Inf 1.197341e-14 1.19744e-14
$Asia$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 3.193949e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 -1.434368e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 -9.061987e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 1.19744e-14
2 -Inf Inf 1.197341e-14 1.19744e-14
3 -Inf Inf 1.197341e-14 1.19744e-14
4 -Inf Inf 1.197341e-14 1.19744e-14
5 -Inf Inf 1.197341e-14 1.19744e-14
6 -Inf Inf 1.197341e-14 1.19744e-14
7 -Inf Inf 1.197341e-14 1.19744e-14
8 -Inf Inf 1.197341e-14 1.19744e-14
9 -Inf Inf 1.197341e-14 1.19744e-14
10 -Inf Inf 1.197341e-14 1.19744e-14
$Asia$d2
$Asia$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
302 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
303 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
304 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
307 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
313 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
316 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
322 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
331 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
332 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
333 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
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335 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
337 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
340 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
342 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
343 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
344 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
345 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
346 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
347 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
348 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
349 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
350 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
351 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
352 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
353 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
358 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
359 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
360 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
361 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
362 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
363 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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365 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
366 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
367 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
368 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
369 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
370 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
371 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
372 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
373 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
374 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
375 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
376 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
377 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
378 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
379 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
380 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
381 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
382 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
383 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
384 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
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386 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
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$Asia$d2$`mpg:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
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38 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
64 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
65 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
66 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
67 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
68 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
69 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
70 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
71 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
72 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
73 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
77 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
78 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
80 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
81 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
82 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
83 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
84 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
85 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
86 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
87 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
88 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
89 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
90 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Asia$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Asia$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
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45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
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73 NA NA NA
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75 NA NA NA
76 NA NA NA
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78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
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240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
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248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
289 NA NA NA
290 NA NA NA
291 NA NA NA
292 NA NA NA
293 NA NA NA
294 NA NA NA
295 NA NA NA
296 NA NA NA
297 NA NA NA
298 NA NA NA
299 NA NA NA
300 NA NA NA
$Asia$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
$Asia$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Asia$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Asia$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Asia$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBBEFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBDBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC5FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #1508FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #A696FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBDBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBC1FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #0000FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBC9FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #A696FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBDBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBC9FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #9984FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFC6BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #A696FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBDBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBC9FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #9984FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFC6BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #BBC9FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #A696FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBDBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #BBC9FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #9A84FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFC5BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #BBC9FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #A290FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBDBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #BBC9FF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #957EFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFC5BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #BBC9FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #A290FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBDBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #BBC9FF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #957EFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFC5BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #BBC9FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #A290FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFAFAA 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #BBC9FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #6546FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFC8BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #BBC9FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #A392FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFAFAA 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #BBC9FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #6546FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #A392FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFB9B8 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #BBCAFF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #8E74FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC7BB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #BBCAFF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #A392FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFB9B8 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #BBCAFF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #8E74FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFC5BB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #BBCAFF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #A696FF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBDBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #BBCAFF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #9A85FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBFBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFCBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #A696FF 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBDBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFCBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #9D89FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBFBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFCBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #A696FF 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBDBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFCBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #9D89FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBFBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFCBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #A696FF 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBDBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFCBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #9D89FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBFBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFCBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #A696FF 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBDBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFCABB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #9E8AFF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBFBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFCBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #A290FF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBDBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFCABB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #9983FF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBFBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFCBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #A290FF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBCBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFCABB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #5B3CFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFC4BB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFCBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #A494FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFACA6 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFC8BB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #5B3CFF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFC4BB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFCBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #A494FF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFB7B5 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFC7BB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #A08EFF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFC1BB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFCBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #A392FF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBEBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFC6BB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #BBBDFF 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFC7BB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #BBC3FF 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFAAA3 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFC2BB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFCABB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FF9F93 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFC3BB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #BBC9FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FF7E68 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFC0BB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #B8B6FF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FF8875 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBFBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #BBCAFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FF5238 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBDBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #7859FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FF8774 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBDBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #BBCBFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FF452C 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFB8B7 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #4429FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FF8774 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBDBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #BBCBFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FF432A 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFB1AC 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #4B2EFF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FF8774 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBDBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #BBCBFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FF0000 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFA398 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #957DFF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FF8571 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBDBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #BBCBFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FF3B23 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFA9A1 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #957DFF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FF8672 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBDBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #BBCBFF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FF3C24 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFA9A1 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #9780FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FF9B8E 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBDBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFC7BB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FF5A40 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFA9A1 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #BBBEFF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FF9080 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBDBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFCABB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FF4C32 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFA9A1 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #AEA4FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FF9080 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBDBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFCABB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FF2412 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFA297 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #AEA4FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
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79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Asia$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF7F6A 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC7BB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #AFA5FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFB8B6 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF432A 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFA399 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCBFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFB8B6 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FF573D 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FF8977 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Asia$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF9C8F 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC5FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF0000 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFCCBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC2FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF8875 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFCCBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBFBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBC2FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #B2ABFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #B8B5FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #AB9EFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #9781FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBC2FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBBCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #BAB9FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #AB9EFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #8A6FFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Asia$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF0100 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFC1BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #BBC9FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #BBC7FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFC4BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #BBC7FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFC4BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #BBC6FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #BBC6FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #BBC5FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFC4BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #BBC6FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFC4BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #BBC6FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Asia$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBBDFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC5BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCABB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBBDFF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFCBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBBDFF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFCBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC0FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #957EFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC5FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #9781FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC6BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC5FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #6242FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC8BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC5FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #866AFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC8BB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC5FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #866AFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC2BB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFCCBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #6748FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC2BB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFCCBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #6748FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC6BB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFCCBB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #B6B1FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFCABB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #B6B1FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFCABB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #B6B1FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFCABB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #B6B1FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFCABB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #B6B1FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFBDBB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFCABB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #8E74FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFBFBB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFCABB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #A89AFF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC2BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFCABB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #AA9DFF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFBDBB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFCABB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #9780FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFBCBB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFCABB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #8F75FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFBFBB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFCABB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #9279FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFBFBB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFCABB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #9D88FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFBFBB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFCABB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #9D88FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFBFBB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFCABB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #9D88FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFBFBB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFCABB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #9D88FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFBFBB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFCABB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #9D88FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFB5B2 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFCABB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #6445FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFBBBB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFCABB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #6A4AFF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFBBBB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFCABB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #6A4AFF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFB0AB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFCABB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #5335FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFB0AB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFCABB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #3921FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCBFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBBDFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC5BB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFCABB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBBDFF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFCBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBBDFF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFCBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC0FF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #957EFF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC5FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #9781FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC6BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC5FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #6242FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC8BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC5FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #866AFF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC8BB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC5FF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #866AFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC2BB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFCCBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #6748FF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC2BB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFCCBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #6748FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC6BB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFCCBB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #B6B1FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC3BB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFCABB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #B6B1FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC3BB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFCABB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #B6B1FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC2BB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFCABB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #B5B0FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC2BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFCABB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #B5B0FF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFBDBB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
146 #FFCABB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
147 #8D73FF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFBFBB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
149 #FFCABB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
150 #A798FF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC2BB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
152 #FFCABB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
153 #A99CFF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFBDBB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
155 #FFC9BB 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
156 #BBBDFF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFBCBB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
158 #FFC8BB 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
159 #BAB9FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFBBBB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
161 #FFC6BB 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBCAFF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFB7B5 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
164 #FFC8BB 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
165 #5B3CFF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFAAA3 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
167 #FFC4BB 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBBFFF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFAAA3 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
170 #FFC4BB 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
171 #BBBFFF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FFA59B 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
173 #FFC3BB 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
174 #BBC0FF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FF988B 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
176 #FFC1BB 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
177 #BBC4FF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FF8E7D 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
179 #FFC4BB 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
180 #5134FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FF9485 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
182 #FFC1BB 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
183 #BBBEFF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF9586 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
185 #FFC2BB 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
186 #BBBDFF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF816C 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
188 #FFC3BB 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
189 #3A21FF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FF654C 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
191 #FFC1BB 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
192 #0000FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFCBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCBFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBBDFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC5BB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
200 #FFCABB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBBDFF 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC8BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
203 #FFCBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBBDFF 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFCBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBC0FF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
207 #957EFF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFC8BB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBC5FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
210 #9781FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFC6BB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBC5FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
213 #6242FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFC8BB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBC5FF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
216 #866AFF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFC8BB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBC5FF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
219 #866AFF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFC2BB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
221 #FFCCBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
222 #6748FF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC2BB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
224 #FFCCBB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
225 #6748FF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC6BB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
227 #FFCCBB 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
228 #B6B1FF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC3BB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
230 #FFCABB 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
231 #B6B1FF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
232 #FFC3BB 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
233 #FFCABB 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
234 #B6B1FF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFC2BB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
236 #FFCABB 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
237 #B5B0FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
238 #FFC2BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
239 #FFC8BB 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
240 #B4ADFF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
241 #FFBDBB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
242 #FFC8BB 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
243 #8B70FF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
244 #FFBFBB 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
245 #FFC8BB 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
246 #A696FF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
247 #FFBEBB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
248 #FFC7BB 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
249 #A899FF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
250 #FFADA7 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
251 #FFC5BB 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
252 #BBBCFF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
253 #FF9E92 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
254 #FFC4BB 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
255 #B9B6FF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
256 #FF9687 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
257 #FFC1BB 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
258 #BBC9FF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
259 #FF8C7A 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
260 #FFC2BB 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
261 #BBBBFF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
262 #FF7B65 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
263 #FFBDBB 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
264 #FFC9BB 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
265 #FF7963 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
266 #FFBDBB 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
267 #FFC9BB 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
268 #FF725B 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
269 #FFBDBB 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
270 #FFC9BB 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
271 #FF644B 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
272 #FFBBBB 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
273 #FFC8BB 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
274 #FF573D 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
275 #FFBEBB 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
276 #B7B4FF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
277 #FF5F45 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
278 #FFB9B8 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
279 #FFC9BB 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
280 #FF6046 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
281 #FFBCBB 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
282 #FFCABB 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
283 #FF452C 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
284 #FFBDBB 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
285 #B0A7FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
286 #FF0000 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
287 #FFBABA 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
288 #A799FF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$Asia$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC4FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC5FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC4FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC2FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFCABB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC4FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #B5B1FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFB1AC 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC4FF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #A493FF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFA79F 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC4FF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #A18FFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FF9E92 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC4FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #9E8BFF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FF9E92 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC4FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #9E8BFF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBCAFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC4FF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBBEFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBCAFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBC4FF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBBEFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBCAFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC4FF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBBEFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBCAFF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC4FF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
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335 #BBC1FF 16 4 1 79 15.5 16.5 3.5 4.5 NA 0.1 1
336 #BBBEFF 16 4 1 79 15.5 16.5 3.5 4.5 NA 0.1 1
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345 #937BFF 19 4 1 94 18.5 19.5 3.5 4.5 NA 0.1 1
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384 #6242FF 32 4 1 159 31.5 32.5 3.5 4.5 NA 0.1 1
385 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
386 #BBC3FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
387 #BBBCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
388 #BBCBFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
389 #BBC2FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
390 #BBBCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
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392 #BBC2FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
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399 #BBBCFF 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
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428 #BBC1FF 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
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435 #967FFF 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
436 #FFC3BB 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
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439 #FFC0BB 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
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alpha width height
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$Asia$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #BBC8FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC7FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC5FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFCABB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC7FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC1FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC9BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC7FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC1FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC8BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC7FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC1FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC8BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC7FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC1FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
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8 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #BBC8FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FF0000 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #BBC5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #BBC9FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #BBCCFF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #BBC7FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #BBC4FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FFBFBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #BBC4FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC3FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #BBCAFF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #BBC6FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBC3FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FF1C0C 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #BBC2FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #BBBCFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #B7B4FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #BBC1FF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #BBBDFF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #B7B4FF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFBEBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #BBBFFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #BBBCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #B8B5FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #BBC1FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #BBBDFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #B7B4FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
$Asia$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #B8B5FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #B9B7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #B8B5FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #B7B4FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #B9B8FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #B8B5FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #B7B3FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #BAB9FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #B7B4FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #B5B0FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #BBBAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #B6B2FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #B4ADFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #BABAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #B6B2FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #B4ADFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FF0000 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #B7B3FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #B4AEFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #BAB9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #B6B1FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #B3ACFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #B8B6FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #B5AFFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #B3ACFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FF0200 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #B7B3FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #B4AEFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #B7B4FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #B4AEFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #B1AAFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #B6B2FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #B3ACFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #B0A7FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #B9B8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #B7B3FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #B5AFFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #B3ADFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #ADA2FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #A595FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #B3ACFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #ACA0FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #A392FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FF0301 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
47 #B7B3FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
48 #B4AEFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
49 #B5B0FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
50 #ADA2FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
51 #A595FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
52 #B3ACFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
53 #ACA0FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
54 #A392FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
55 #FF0301 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
56 #B7B3FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
57 #B4AEFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
58 #B5B0FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
59 #ADA2FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
60 #A595FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
61 #B1A9FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
62 #ACA0FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
63 #A392FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
64 #B9B7FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
65 #B7B3FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
66 #B5AFFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
67 #B2ABFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
68 #ADA2FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
69 #A596FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
70 #B1A8FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
71 #ACA0FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
72 #A392FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
73 #FF0301 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
74 #B7B3FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
75 #B4AEFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
76 #B5B0FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
77 #ADA2FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
78 #A595FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
79 #B3ACFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
80 #ACA0FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
81 #A392FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
82 #FF0301 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
83 #B7B3FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
84 #B4AEFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
85 #B5B0FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
86 #ADA2FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
87 #A595FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
88 #B3ACFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
89 #ACA0FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
90 #A392FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Asia$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF4C32 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF755E 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCCFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FF1608 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCCFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF0000 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCCFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FF9A8D 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FFC2BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF9A8D 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #BBC9FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FF9A8D 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBC9FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FFB4B1 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #BBCCFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #BBCBFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBC9FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FFC9BB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #BBCBFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #BBC8FF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FFC9BB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #BBCBFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBC8FF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #BBCCFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #FFC0BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #BBCBFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #BBC9FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #BBCCFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #BBCAFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #BBC8FF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #BBCCFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #BBCAFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #BBC8FF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FFC5BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #BBCBFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #BBCBFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #BBCAFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #BBC8FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #BBCCFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #BBCAFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #BBC8FF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #BBCCFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #BBCAFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #BBC8FF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #BBCCFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FFC0BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #BBCBFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBC8FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #BBCBFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #BBCAFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #BBC8FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #BBCCFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #BBCAFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #BBC8FF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #BBCCFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #BBCAFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #BBC8FF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FFC0BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #BBCBFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #BBC8FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #BBCCFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #BBCAFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #BBC8FF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #BBCCFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #BBCAFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #BBC8FF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FFC6BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBC9FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #BBCBFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #BBCAFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #BBC9FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #BBCCFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #BBCAFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #BBC8FF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #BBCCFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #BBCAFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #BBC8FF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #BBCCFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FFC0BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #BBCBFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #BBCBFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #BBCAFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #BBC8FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #BBCCFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #BBCAFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #BBC8FF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #BBCCFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #BBCAFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #BBC8FF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #BBCCFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FFC0BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #BBCBFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #BBCBFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #BBCAFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #BBC8FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #BBCCFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #BBCAFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #BBC8FF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #BBCCFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #BBCAFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #BBC8FF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Asia$eff
$Asia$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 1.197341e-14
2 2 1 2 NA lightgray 0.5 1 NA 1.197341e-14
3 3 1 3 NA lightgray 0.5 1 NA 1.197341e-14
4 4 1 4 NA lightgray 0.5 1 NA 1.197341e-14
5 5 1 5 NA lightgray 0.5 1 NA 1.197341e-14
6 6 1 6 NA lightgray 0.5 1 NA 1.197341e-14
7 7 1 7 NA lightgray 0.5 1 NA 1.197341e-14
xmax ymin ymax
1 1.19744e-14 -Inf Inf
2 1.19744e-14 -Inf Inf
3 1.19744e-14 -Inf Inf
4 1.19744e-14 -Inf Inf
5 1.19744e-14 -Inf Inf
6 1.19744e-14 -Inf Inf
7 1.19744e-14 -Inf Inf
$Asia$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 1.197411e-14 1.197411e-14 1 1 1 0.875 1.125 black 0.5
2 1.197411e-14 1.197411e-14 2 1 2 1.875 2.125 black 0.5
3 -1.178021e-05 3.193949e-05 3 1 3 2.875 3.125 black 0.5
4 -1.894153e-03 5.489234e-03 4 1 4 3.875 4.125 black 0.5
5 -8.380929e-03 3.090750e-02 5 1 5 4.875 5.125 black 0.5
6 -2.322303e-01 1.187455e-01 6 1 6 5.875 6.125 black 0.5
7 -1.939198e+00 NA 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$Asia$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 1.197390e-14 1.197390e-14 0.7 1.3 1 1 1 NA white 0.5
2 1.197390e-14 1.197390e-14 1.7 2.3 2 1 2 NA white 0.5
3 -5.907412e-06 5.907412e-06 2.7 3.3 3 1 3 NA white 0.5
4 -8.579039e-04 8.579039e-04 3.7 4.3 4 1 4 NA white 0.5
5 -1.087804e-02 1.087804e-02 4.7 5.3 5 1 5 NA white 0.5
6 -7.015474e-02 7.015474e-02 5.7 6.3 6 1 6 NA white 0.5
7 -5.844910e-01 5.844910e-01 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$Asia$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.19739e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 1.19739e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 1.19739e-14 NALED 47.8% 3 1 3 black 3 0 0.5 -1 NA
4 1.19739e-14 NALED 40.9% 4 1 4 black 3 0 0.5 -1 NA
5 1.19739e-14 NALED 38.9% 5 1 5 black 3 0 0.5 -1 NA
6 1.19739e-14 NALED 37.7% 6 1 6 black 3 0 0.5 -1 NA
7 1.19739e-14 NALED 43.0% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.197390e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.197390e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -5.907412e-06 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -8.579039e-04 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.087804e-02 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -7.015474e-02 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 -5.844910e-01 ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.197390e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.197390e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 5.907412e-06 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 8.579039e-04 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.087804e-02 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 7.015474e-02 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 5.844910e-01 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.19739e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 1.19739e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 1.19739e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 1.19739e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 1.19739e-14 ALED 0.02 5 1 5 black 3 0 0.5 2 NA
6 1.19739e-14 ALED 0.14 6 1 6 black 3 0 0.5 2 NA
7 1.19739e-14 ALED 1.17 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Asia$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 1 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$Europe
$Europe$d1
$Europe$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.305116e-14 1.305215e-14
2 -Inf Inf 1.305116e-14 1.305215e-14
3 -Inf Inf 1.305116e-14 1.305215e-14
4 -Inf Inf 1.305116e-14 1.305215e-14
5 -Inf Inf 1.305116e-14 1.305215e-14
6 -Inf Inf 1.305116e-14 1.305215e-14
7 -Inf Inf 1.305116e-14 1.305215e-14
8 -Inf Inf 1.305116e-14 1.305215e-14
9 -Inf Inf 1.305116e-14 1.305215e-14
10 -Inf Inf 1.305116e-14 1.305215e-14
$Europe$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.305186e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.305186e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
$Europe$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 -0.00618887 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 -0.01548244 1 2 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.305116e-14 1.305215e-14
2 1.305116e-14 1.305215e-14
$Europe$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.21712565 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.26805387 1 2 NA lightgray 0.5 1 NA -Inf
3 3 0.93059625 1 3 NA lightgray 0.5 1 NA -Inf
4 4 0.76392958 1 4 NA lightgray 0.5 1 NA -Inf
5 5 -0.06882970 1 5 NA lightgray 0.5 1 NA -Inf
6 6 0.09783696 1 6 NA lightgray 0.5 1 NA -Inf
7 7 0.09783696 1 7 NA lightgray 0.5 1 NA -Inf
8 8 0.59590470 1 8 NA lightgray 0.5 1 NA -Inf
9 9 -0.23738828 1 9 NA lightgray 0.5 1 NA -Inf
10 10 -0.08414960 1 10 NA lightgray 0.5 1 NA -Inf
11 11 0.24918374 1 11 NA lightgray 0.5 1 NA -Inf
12 12 0.41585053 1 12 NA lightgray 0.5 1 NA -Inf
13 13 0.41585053 1 13 NA lightgray 0.5 1 NA -Inf
14 14 0.25025654 1 14 NA lightgray 0.5 1 NA -Inf
15 15 0.75025654 1 15 NA lightgray 0.5 1 NA -Inf
16 16 0.44517303 1 16 NA lightgray 0.5 1 NA -Inf
17 17 1.32301938 1 17 NA lightgray 0.5 1 NA -Inf
18 18 1.15711598 1 18 NA lightgray 0.5 1 NA -Inf
19 19 0.99044927 1 19 NA lightgray 0.5 1 NA -Inf
20 20 0.95687941 1 20 NA lightgray 0.5 1 NA -Inf
21 21 1.13019402 1 21 NA lightgray 0.5 1 NA -Inf
22 22 1.63702005 1 22 NA lightgray 0.5 1 NA -Inf
23 23 1.30593413 1 23 NA lightgray 0.5 1 NA -Inf
24 24 1.97260071 1 24 NA lightgray 0.5 1 NA -Inf
25 25 1.97260070 1 25 NA lightgray 0.5 1 NA -Inf
26 26 2.30593426 1 26 NA lightgray 0.5 1 NA -Inf
27 27 2.63319411 1 27 NA lightgray 0.5 1 NA -Inf
28 28 2.07246592 1 28 NA lightgray 0.5 1 NA -Inf
29 29 2.57245050 1 29 NA lightgray 0.5 1 NA -Inf
30 30 2.23178490 1 30 NA lightgray 0.5 1 NA -Inf
31 31 1.89843292 1 31 NA lightgray 0.5 1 NA -Inf
32 32 2.56509702 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
3 Inf 1.305116e-14 1.305215e-14
4 Inf 1.305116e-14 1.305215e-14
5 Inf 1.305116e-14 1.305215e-14
6 Inf 1.305116e-14 1.305215e-14
7 Inf 1.305116e-14 1.305215e-14
8 Inf 1.305116e-14 1.305215e-14
9 Inf 1.305116e-14 1.305215e-14
10 Inf 1.305116e-14 1.305215e-14
11 Inf 1.305116e-14 1.305215e-14
12 Inf 1.305116e-14 1.305215e-14
13 Inf 1.305116e-14 1.305215e-14
14 Inf 1.305116e-14 1.305215e-14
15 Inf 1.305116e-14 1.305215e-14
16 Inf 1.305116e-14 1.305215e-14
17 Inf 1.305116e-14 1.305215e-14
18 Inf 1.305116e-14 1.305215e-14
19 Inf 1.305116e-14 1.305215e-14
20 Inf 1.305116e-14 1.305215e-14
21 Inf 1.305116e-14 1.305215e-14
22 Inf 1.305116e-14 1.305215e-14
23 Inf 1.305116e-14 1.305215e-14
24 Inf 1.305116e-14 1.305215e-14
25 Inf 1.305116e-14 1.305215e-14
26 Inf 1.305116e-14 1.305215e-14
27 Inf 1.305116e-14 1.305215e-14
28 Inf 1.305116e-14 1.305215e-14
29 Inf 1.305116e-14 1.305215e-14
30 Inf 1.305116e-14 1.305215e-14
31 Inf 1.305116e-14 1.305215e-14
32 Inf 1.305116e-14 1.305215e-14
$Europe$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 -0.2389154 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 0.2471940 1 2 NA lightgray 0.5 1 NA -Inf Inf
3 3 0.3799475 1 3 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 1.305116e-14 1.305215e-14
2 1.305116e-14 1.305215e-14
3 1.305116e-14 1.305215e-14
$Europe$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.005489341 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 0.001898392 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 0.001898401 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 0.001898401 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 0.001898406 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.305116e-14 1.305215e-14
2 Inf 1.305116e-14 1.305215e-14
3 Inf 1.305116e-14 1.305215e-14
4 Inf 1.305116e-14 1.305215e-14
5 Inf 1.305116e-14 1.305215e-14
$Europe$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 -3.194015e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 1.434104e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 9.061769e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.305116e-14 1.305215e-14
2 -Inf Inf 1.305116e-14 1.305215e-14
3 -Inf Inf 1.305116e-14 1.305215e-14
4 -Inf Inf 1.305116e-14 1.305215e-14
5 -Inf Inf 1.305116e-14 1.305215e-14
6 -Inf Inf 1.305116e-14 1.305215e-14
7 -Inf Inf 1.305116e-14 1.305215e-14
8 -Inf Inf 1.305116e-14 1.305215e-14
9 -Inf Inf 1.305116e-14 1.305215e-14
10 -Inf Inf 1.305116e-14 1.305215e-14
$Europe$d2
$Europe$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
302 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
303 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
304 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
307 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
313 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
316 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
322 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
331 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
332 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
333 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
334 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
335 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
337 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
340 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
342 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
343 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
344 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
345 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
346 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
347 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
348 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
349 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
350 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
351 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
352 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
353 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
358 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
359 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
360 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
361 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
362 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
363 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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365 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
366 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
367 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
368 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
369 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
370 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
371 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
372 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
373 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
374 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
375 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
376 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
377 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
378 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
379 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
380 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
381 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
382 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
383 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
384 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
385 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
386 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
387 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
388 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
389 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
390 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
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392 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
393 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
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395 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
396 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
397 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
398 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
399 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
400 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
401 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
402 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
403 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
404 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
405 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
406 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
407 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
408 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
409 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
410 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
411 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
412 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
413 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
414 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
415 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
416 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
417 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
418 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
419 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
420 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
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423 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
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425 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
426 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
427 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
428 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
429 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
430 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
431 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
432 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
433 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
434 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
435 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
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437 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
438 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
439 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
440 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
441 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
442 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
443 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
445 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
446 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
447 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
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449 #FFBBBB 10 15 1 303 9.5 10.5 14.5 15.5 NA 0.1 1
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459 #FFBBBB 3 16 1 80 2.5 3.5 15.5 16.5 NA 0.1 1
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462 #FFBBBB 4 16 1 112 3.5 4.5 15.5 16.5 NA 0.1 1
463 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
464 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
465 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
466 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
467 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
468 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
469 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
470 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
471 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
472 #FFBBBB 8 16 1 240 7.5 8.5 15.5 16.5 NA 0.1 1
473 #FFBBBB 8 16 1 240 7.5 8.5 15.5 16.5 NA 0.1 1
474 #FFBBBB 8 16 1 240 7.5 8.5 15.5 16.5 NA 0.1 1
475 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
476 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
477 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
478 #FFBBBB 10 16 1 304 9.5 10.5 15.5 16.5 NA 0.1 1
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480 #FFBBBB 10 16 1 304 9.5 10.5 15.5 16.5 NA 0.1 1
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$Europe$d2$`mpg:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
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90 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
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6 NA NA
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11 NA NA
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$Europe$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
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7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
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22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
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69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Europe$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
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213 NA NA NA
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222 NA NA NA
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$Europe$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
$Europe$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$Europe$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$Europe$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Europe$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF8F7F 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBBFFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC5BB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
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$Europe$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
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width height
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$Europe$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
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width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$Europe$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBFBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFC7BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFB3B0 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFC7BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFB1AD 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFC0BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFA196 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFAEA8 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBDBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FF8A78 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFB6B4 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF5F45 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FF674E 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FF6C53 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FF8976 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFB6B4 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FF3A22 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FF573D 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FF6950 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FF8976 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFB6B4 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FF301A 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FF4F35 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FF684F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FF8A78 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFB6B4 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFC0BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FF331D 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FF4F35 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FF684F 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FF8976 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFB6B4 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FF4F35 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FF6950 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FF8976 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFB6B4 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FF4F35 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FF6950 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$Europe$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC1BB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC1BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC5FF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC1BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCAFF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC5FF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF998C 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCCFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC8FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBEBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCCFF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC8FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFAAA2 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCCFF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC8FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFAAA2 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCCFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC8FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCCFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC8FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBEBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCBFF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC7FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBEBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCBFF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC8FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FF6F57 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCBFF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC4FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FF6F57 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCBFF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC4FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FF0000 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBCCFF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC4FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFABA4 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC9FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC2FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFABA4 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBC9FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC2FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC4BB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBC9FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC6FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFBCBB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBC9FF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC5FF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFBCBB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBC9FF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC5FF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFC6BB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBC9FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC5FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFC3BB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBC9FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC5FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FF9789 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBC9FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC4FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FF9789 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBC9FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC4FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FF9789 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBC9FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC4FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FF9789 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBC9FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC4FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FF9789 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBC9FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC4FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FF9383 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBC9FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC1FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFBDBB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBC9FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC1FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFA59C 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBC9FF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC1FF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFA59C 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBC9FF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC1FF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFBDBB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBC9FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC3FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFBDBB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBC9FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC3FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC1BB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCCFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBCAFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC1BB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBCAFF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC5FF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC1BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBCAFF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC5FF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FF998C 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBCCFF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC8FF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFBEBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBCCFF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC8FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFAAA2 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBCCFF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC8FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFAAA2 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBCCFF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC8FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBCCFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC8FF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFBEBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBCBFF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC7FF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFBEBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBCBFF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC8FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FF6F57 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBCBFF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC4FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FF6F57 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBCBFF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC4FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FF0000 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBCCFF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC4FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFA79F 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBC9FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC2FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC5BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBC6FF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBBFFF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #BBCBFF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBC6FF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBC3FF 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFCABB 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBC6FF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
150 #BBC2FF 18 2 1 53 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFCABB 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBC6FF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
153 #BBC2FF 19 2 1 56 18.5 19.5 1.5 2.5 NA 0.1 1
154 #BBC8FF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
155 #BBC6FF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
156 #BBC5FF 20 2 1 59 19.5 20.5 1.5 2.5 NA 0.1 1
157 #BBC8FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
158 #BBC5FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
159 #BBC2FF 21 2 1 62 20.5 21.5 1.5 2.5 NA 0.1 1
160 #BBC9FF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
161 #BBC3FF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBBCFF 22 2 1 65 21.5 22.5 1.5 2.5 NA 0.1 1
163 #BBCAFF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
164 #BBC5FF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBBDFF 23 2 1 68 22.5 23.5 1.5 2.5 NA 0.1 1
166 #BBC6FF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
167 #BBC1FF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
168 #B7B4FF 24 2 1 71 23.5 24.5 1.5 2.5 NA 0.1 1
169 #BBC6FF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
170 #BBC1FF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
171 #B7B4FF 25 2 1 74 24.5 25.5 1.5 2.5 NA 0.1 1
172 #BBC6FF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
173 #BBC0FF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
174 #B3ADFF 26 2 1 77 25.5 26.5 1.5 2.5 NA 0.1 1
175 #BBC6FF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
176 #BBBEFF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
177 #B2AAFF 27 2 1 80 26.5 27.5 1.5 2.5 NA 0.1 1
178 #BBC3FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
179 #BBC1FF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
180 #BBBDFF 28 2 1 83 27.5 28.5 1.5 2.5 NA 0.1 1
181 #BBC2FF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
182 #BBBEFF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
183 #BBBBFF 29 2 1 86 28.5 29.5 1.5 2.5 NA 0.1 1
184 #BBC5FF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
185 #BBBEFF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
186 #B4AEFF 30 2 1 89 29.5 30.5 1.5 2.5 NA 0.1 1
187 #BBC5FF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
188 #BBBFFF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
189 #B4AEFF 31 2 1 92 30.5 31.5 1.5 2.5 NA 0.1 1
190 #BBC5FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
191 #BBBCFF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
192 #A493FF 32 2 1 95 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFC1BB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCCFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBCAFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC1BB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
200 #BBCAFF 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBC5FF 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC1BB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
203 #BBCAFF 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBC5FF 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FF998C 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBCCFF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
207 #BBC8FF 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFBEBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBCCFF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
210 #BBC8FF 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FFAAA2 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBCCFF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
213 #BBC8FF 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FFAAA2 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBCCFF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
216 #BBC8FF 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBCCFF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
219 #BBC8FF 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FFBEBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
221 #BBCBFF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
222 #BBC7FF 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFBEBB 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
224 #BBCBFF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
225 #BBC8FF 11 3 1 33 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FF6F57 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
227 #BBCBFF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
228 #BBC4FF 12 3 1 36 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FF6F57 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
230 #BBCBFF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
231 #BBC4FF 13 3 1 39 12.5 13.5 2.5 3.5 NA 0.1 1
232 #FF0000 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
233 #BBCCFF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
234 #BBC4FF 14 3 1 42 13.5 14.5 2.5 3.5 NA 0.1 1
235 #FFA79F 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
236 #BBC9FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
237 #BBC2FF 15 3 1 45 14.5 15.5 2.5 3.5 NA 0.1 1
238 #BBCBFF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
239 #BBC5FF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
240 #BBBFFF 16 3 1 48 15.5 16.5 2.5 3.5 NA 0.1 1
241 #BBC8FF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
242 #BBC2FF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
243 #BBBEFF 17 3 1 51 16.5 17.5 2.5 3.5 NA 0.1 1
244 #BBCAFF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
245 #BBC2FF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
246 #BBBEFF 18 3 1 54 17.5 18.5 2.5 3.5 NA 0.1 1
247 #BBCAFF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
248 #BBC2FF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
249 #BBBEFF 19 3 1 57 18.5 19.5 2.5 3.5 NA 0.1 1
250 #BBC5FF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
251 #BBC2FF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
252 #BBC1FF 20 3 1 60 19.5 20.5 2.5 3.5 NA 0.1 1
253 #BBC5FF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
254 #BBC1FF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
255 #BBBCFF 21 3 1 63 20.5 21.5 2.5 3.5 NA 0.1 1
256 #BBC6FF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
257 #BBBEFF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
258 #AA9CFF 22 3 1 66 21.5 22.5 2.5 3.5 NA 0.1 1
259 #BBC6FF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
260 #BBBFFF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
261 #A18FFF 23 3 1 69 22.5 23.5 2.5 3.5 NA 0.1 1
262 #BBC3FF 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
263 #B7B3FF 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
264 #896DFF 24 3 1 72 23.5 24.5 2.5 3.5 NA 0.1 1
265 #BBC3FF 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
266 #B2ABFF 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
267 #7556FF 25 3 1 75 24.5 25.5 2.5 3.5 NA 0.1 1
268 #BBC3FF 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
269 #AA9DFF 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
270 #4D30FF 26 3 1 78 25.5 26.5 2.5 3.5 NA 0.1 1
271 #BBC3FF 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
272 #A594FF 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
273 #492DFF 27 3 1 81 26.5 27.5 2.5 3.5 NA 0.1 1
274 #BBC1FF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
275 #ADA2FF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
276 #6B4BFF 28 3 1 84 27.5 28.5 2.5 3.5 NA 0.1 1
277 #BBBFFF 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
278 #A594FF 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
279 #6041FF 29 3 1 87 28.5 29.5 2.5 3.5 NA 0.1 1
280 #BBC2FF 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
281 #A594FF 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
282 #4F32FF 30 3 1 90 29.5 30.5 2.5 3.5 NA 0.1 1
283 #BBC0FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
284 #A799FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
285 #5033FF 31 3 1 93 30.5 31.5 2.5 3.5 NA 0.1 1
286 #BBC0FF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
287 #9E8BFF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
288 #0000FF 32 3 1 96 31.5 32.5 2.5 3.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$Europe$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFC6BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFC6BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFC6BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFC6BB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFC6BB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC4BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFC6BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFCABB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBFBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFC6BB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC3FF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBCBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFC6BB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #B4AEFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBCBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFC6BB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #B4AEFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFC6BB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #AFA6FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFC6BB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #AFA6FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC3BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFC6BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #FFCABB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFC6BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #FFCABB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFC6BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #FFCABB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFC6BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
45 #FFCABB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFC6BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
48 #FFCABB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC0BB 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFC6BB 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC5FF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFB7B6 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFC6BB 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
54 #A18EFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFB1AD 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFC6BB 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
57 #9076FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFB0AB 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFC6BB 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
60 #8B70FF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFAFAA 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFC6BB 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
63 #896EFF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC2BB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFC6BB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
66 #FFCBBB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC2BB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFC6BB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
69 #FFCBBB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC2BB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFC6BB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
72 #FFCBBB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC2BB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFC6BB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
75 #FFCBBB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC2BB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFC6BB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
78 #FFCBBB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFC1BB 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFC6BB 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBCBFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFBCBB 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFC6BB 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
84 #B3ACFF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFB0AB 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFC6BB 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
87 #8A6EFF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFAEA8 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFC6BB 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
90 #8265FF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFACA5 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFC6BB 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
93 #7B5DFF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFACA5 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFC6BB 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
96 #7B5DFF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFC0BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC0BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
101 #FFC6BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC1FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC0BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFC6BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC1FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC0BB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFC6BB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC1FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC0BB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
110 #FFC6BB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC1FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFBEBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
113 #FFC6BB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
114 #AB9FFF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFB5B2 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
116 #FFC6BB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
117 #7556FF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFA8A0 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
119 #FFC6BB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
120 #2A16FF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFA8A0 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
122 #FFC6BB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
123 #2915FF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFA69D 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFC6BB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
126 #0000FF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFA69D 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFC6BB 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
129 #0000FF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC0BB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFC6BB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC1FF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC0BB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFC6BB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC1FF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC0BB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFC6BB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC1FF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFBFBB 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFC6BB 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC1FF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFBFBB 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFC5BB 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
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427 #FFB4B1 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
428 #FFC2BB 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
429 #BBC3FF 15 5 1 75 14.5 15.5 4.5 5.5 NA 0.1 1
430 #FFADA7 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
431 #FFC0BB 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
432 #BBC3FF 16 5 1 80 15.5 16.5 4.5 5.5 NA 0.1 1
433 #FF9889 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
434 #FFBFBB 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
435 #AFA6FF 17 5 1 85 16.5 17.5 4.5 5.5 NA 0.1 1
436 #FF7760 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
437 #FFBCBB 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
438 #8062FF 18 5 1 90 17.5 18.5 4.5 5.5 NA 0.1 1
439 #FF6349 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
440 #FFB8B7 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
441 #6D4EFF 19 5 1 95 18.5 19.5 4.5 5.5 NA 0.1 1
442 #FF553B 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
443 #FFB3AF 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
444 #6849FF 20 5 1 100 19.5 20.5 4.5 5.5 NA 0.1 1
445 #FF462D 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
446 #FFACA5 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
447 #7353FF 21 5 1 105 20.5 21.5 4.5 5.5 NA 0.1 1
448 #FF765F 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
449 #FFA79E 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
450 #FFCABB 22 5 1 110 21.5 22.5 4.5 5.5 NA 0.1 1
451 #FF735B 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
452 #FFA297 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
453 #FFC9BB 23 5 1 115 22.5 23.5 4.5 5.5 NA 0.1 1
454 #FF6E56 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
455 #FF9F93 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
456 #FFC9BB 24 5 1 120 23.5 24.5 4.5 5.5 NA 0.1 1
457 #FF684F 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
458 #FF9B8E 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
459 #FFC9BB 25 5 1 125 24.5 25.5 4.5 5.5 NA 0.1 1
460 #FF654B 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
461 #FF998B 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
462 #FFC9BB 26 5 1 130 25.5 26.5 4.5 5.5 NA 0.1 1
463 #FF5C42 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
464 #FF9688 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
465 #FFCABB 27 5 1 135 26.5 27.5 4.5 5.5 NA 0.1 1
466 #FF3E26 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
467 #FF9586 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
468 #BBBFFF 28 5 1 140 27.5 28.5 4.5 5.5 NA 0.1 1
469 #FF190A 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
470 #FF9485 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
471 #A291FF 29 5 1 145 28.5 29.5 4.5 5.5 NA 0.1 1
472 #FF0E05 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
473 #FF9484 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
474 #9F8CFF 30 5 1 150 29.5 30.5 4.5 5.5 NA 0.1 1
475 #FF0000 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
476 #FF9484 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
477 #9B86FF 31 5 1 155 30.5 31.5 4.5 5.5 NA 0.1 1
478 #FF0000 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
479 #FF9484 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
480 #9A84FF 32 5 1 160 31.5 32.5 4.5 5.5 NA 0.1 1
alpha width height
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$Europe$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFC8BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFC8BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFC8BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFC8BB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFC8BB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFC8BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFC9BB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC7BB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFC8BB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFCABB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC5BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFC8BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBCCFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC5BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFC8BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBCBFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC5BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFC8BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBCBFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC5BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFC8BB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBCBFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #FFC8BB 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #FFC8BB 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #FFC8BB 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #FFC8BB 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #FFC8BB 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFC8BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFC8BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #FFC9BB 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFC5BB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFC8BB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #FFCCBB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC5BB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFC8BB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBCCFF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFC5BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFC8BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBCBFF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFC5BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFC8BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBCBFF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #FFC8BB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
69 #FFC8BB 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #FFC8BB 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
75 #FFC8BB 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
78 #FFC8BB 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #FFC8BB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFC8BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFC8BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #FFC9BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFC5BB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFC8BB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBCCFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFC5BB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFC8BB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBCBFF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFC4BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFC8BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBCBFF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFC4BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFC8BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBCBFF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #FFC8BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #FFC8BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #FFC8BB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #FFC8BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #FFC9BB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC7BB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #FFC8BB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #FFCABB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC5BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #FFC8BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBCBFF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC4BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #FFC8BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBCAFF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC4BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFC8BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FF9C8F 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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41 #BBCCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
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43 #FF6148 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
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width height
1 NA NA
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$Europe$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FF8A78 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFB9B7 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
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19 #FF432A 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #FFB9B7 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #FF432A 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #FFB9B7 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC8FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #FF0301 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
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27 #BBC8FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
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29 #FFB9B7 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC8FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #FF8A78 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #FFB9B7 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #BBCAFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #FF654B 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #FFAAA3 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FF765F 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #FFB9B7 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #FF664D 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #FFB9B7 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #FF0000 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #FFAAA3 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #BBC8FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FF432A 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
47 #FFB9B7 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
48 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
49 #FF432A 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
50 #FFB9B7 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
51 #BBC8FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
52 #FF0000 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
53 #FFAAA3 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
54 #BBC8FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
55 #FF432A 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
56 #FFB9B7 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
57 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
58 #FF432A 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
59 #FFB9B7 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
60 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
61 #FF725A 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
62 #FFAAA3 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
63 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
64 #FF7F69 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
65 #FFB9B7 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
66 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
67 #FF765F 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
68 #FFB9B7 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
69 #BBCAFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
70 #FF482E 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
71 #FFAAA3 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
72 #BBC9FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
73 #FF432A 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
74 #FFB9B7 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
75 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
76 #FF432A 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
77 #FFB9B7 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
78 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
79 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
80 #FFAAA3 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
81 #BBC8FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
82 #FF432A 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
83 #FFB9B7 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
84 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
85 #FF432A 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
86 #FFB9B7 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
87 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
88 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
89 #FFAAA3 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
90 #BBC8FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$Europe$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF9180 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFB4B1 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF816C 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFB4B1 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF7A63 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFB4B1 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFADA6 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFB4B1 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCCFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF7962 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFB4B1 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FF836F 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #FFB4B1 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FF4C32 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #FF9E92 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FF4C32 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #FF9E92 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF8875 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #FF9E92 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #FFB0AB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FF4C32 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #FF9E92 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FF836F 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #FFB4B1 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FF4129 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #FF9789 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #FF2110 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #FF8C7A 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBCCFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FF6F57 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #FF8C7A 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #FFA8A0 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FF2110 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #FF8C7A 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBCCFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FF836F 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #FFB4B1 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #FF351F 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #FF9282 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBCCFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #FF301B 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #FF8773 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #FFACA5 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #FF6D55 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #FF8773 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #FFA79E 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #FF1F0F 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #FF8773 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #FFB2AE 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FF8976 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #FFB4B1 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FF3F26 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #FF9282 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBCCFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #FF0000 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #FF8673 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #FFB7B5 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #FF664D 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #FF8673 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #FFA79E 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #FF0000 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #FF8673 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #FFB7B5 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FF836F 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #FFB4B1 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FF351F 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #FF9282 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBCCFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #FF0000 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #FF8673 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #FFB7B5 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #FF664D 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #FF8673 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #FFA79E 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #FF0000 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #FF8673 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #FFB7B5 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FF836F 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #FFB4B1 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FF351F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #FF9282 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBCCFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #FF0000 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #FF8673 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #FFB7B5 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #FF745C 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #FF8673 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #FFA69D 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #FF301B 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #FF8673 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #FFABA4 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #FF8A77 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #FFB4B1 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FF4028 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #FF9282 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBCCFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #FF1C0C 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #FF8673 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #FFB2AE 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #FF664D 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #FF8673 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #FFA79E 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #FF0000 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #FF8673 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #FFB7B5 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FF836F 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #FFB4B1 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FF351F 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #FF9282 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBCCFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #FF8673 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #FFB7B5 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #FF664D 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #FF8673 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #FFA79E 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #FF0000 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #FF8673 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #FFB7B5 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FF836F 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #FFB4B1 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FF351F 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #FF9282 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBCCFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #FF8673 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #FFB7B5 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #FF664D 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #FF8673 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #FFA79E 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #FF0000 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #FF8673 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #FFB7B5 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$Europe$eff
$Europe$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 1.305116e-14
2 2 1 2 NA lightgray 0.5 1 NA 1.305116e-14
3 3 1 3 NA lightgray 0.5 1 NA 1.305116e-14
4 4 1 4 NA lightgray 0.5 1 NA 1.305116e-14
5 5 1 5 NA lightgray 0.5 1 NA 1.305116e-14
6 6 1 6 NA lightgray 0.5 1 NA 1.305116e-14
7 7 1 7 NA lightgray 0.5 1 NA 1.305116e-14
xmax ymin ymax
1 1.305215e-14 -Inf Inf
2 1.305215e-14 -Inf Inf
3 1.305215e-14 -Inf Inf
4 1.305215e-14 -Inf Inf
5 1.305215e-14 -Inf Inf
6 1.305215e-14 -Inf Inf
7 1.305215e-14 -Inf Inf
$Europe$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 1.305186e-14 1.305186e-14 1 1 1 0.875 1.125 black 0.5
2 1.305186e-14 1.305186e-14 2 1 2 1.875 2.125 black 0.5
3 -3.194015e-05 1.177986e-05 3 1 3 2.875 3.125 black 0.5
4 -5.489341e-03 1.894194e-03 4 1 4 3.875 4.125 black 0.5
5 -3.090750e-02 8.380929e-03 5 1 5 4.875 5.125 black 0.5
6 -1.722488e-01 3.647938e-01 6 1 6 5.875 6.125 black 0.5
7 -6.800969e-01 NA 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$Europe$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 1.305165e-14 1.305165e-14 0.7 1.3 1 1 1 NA white 0.5
2 1.305165e-14 1.305165e-14 1.7 2.3 2 1 2 NA white 0.5
3 -5.907267e-06 5.907267e-06 2.7 3.3 3 1 3 NA white 0.5
4 -8.579205e-04 8.579205e-04 3.7 4.3 4 1 4 NA white 0.5
5 -1.087804e-02 1.087804e-02 4.7 5.3 5 1 5 NA white 0.5
6 -1.157814e-01 1.157814e-01 5.7 6.3 6 1 6 NA white 0.5
7 NA 1.136806e+00 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$Europe$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 1.305165e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 1.305165e-14 NALED 10.7% 3 1 3 black 3 0 0.5 -1 NA
4 1.305165e-14 NALED 27.4% 4 1 4 black 3 0 0.5 -1 NA
5 1.305165e-14 NALED 32.2% 5 1 5 black 3 0 0.5 -1 NA
6 1.305165e-14 NALED 30.2% 6 1 6 black 3 0 0.5 -1 NA
7 1.305165e-14 NALED 45.9% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.305165e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -5.907267e-06 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -8.579205e-04 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.087804e-02 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -1.157814e-01 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 NA ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 1.305165e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 5.907267e-06 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 8.579205e-04 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.087804e-02 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 1.157814e-01 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 1.136806e+00 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 1.305165e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 1.305165e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 1.305165e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 1.305165e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 1.305165e-14 ALED 0.02 5 1 5 black 3 0 0.5 2 NA
6 1.305165e-14 ALED 0.23 6 1 6 black 3 0 0.5 2 NA
7 1.305165e-14 ALED 2.27 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$Europe$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 1 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$`North America`
$`North America`$d1
$`North America`$d1$mpg
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 2.637774e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 2.637704e-14 2.637802e-14
2 -Inf Inf 2.637704e-14 2.637802e-14
3 -Inf Inf 2.637704e-14 2.637802e-14
4 -Inf Inf 2.637704e-14 2.637802e-14
5 -Inf Inf 2.637704e-14 2.637802e-14
6 -Inf Inf 2.637704e-14 2.637802e-14
7 -Inf Inf 2.637704e-14 2.637802e-14
8 -Inf Inf 2.637704e-14 2.637802e-14
9 -Inf Inf 2.637704e-14 2.637802e-14
10 -Inf Inf 2.637704e-14 2.637802e-14
$`North America`$d1$vs
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 2.637774e-14 1 1 NA lightgray 0.5 1 NA -Inf
2 2 2.637774e-14 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$am
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -5.571100e-10 1 1 NA lightgray 0.5 1 NA -Inf
2 2 1.565414e-10 1 2 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$model
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 -0.19456011 1 1 NA lightgray 0.5 1 NA -Inf
2 2 -0.36122678 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.69534840 1 3 NA lightgray 0.5 1 NA -Inf
4 4 -0.52868173 1 4 NA lightgray 0.5 1 NA -Inf
5 5 -0.02868173 1 5 NA lightgray 0.5 1 NA -Inf
6 6 -0.36201506 1 6 NA lightgray 0.5 1 NA -Inf
7 7 -0.36201506 1 7 NA lightgray 0.5 1 NA -Inf
8 8 -0.86201506 1 8 NA lightgray 0.5 1 NA -Inf
9 9 -0.19534839 1 9 NA lightgray 0.5 1 NA -Inf
10 10 -0.36201506 1 10 NA lightgray 0.5 1 NA -Inf
11 11 -0.69534839 1 11 NA lightgray 0.5 1 NA -Inf
12 12 -0.86201518 1 12 NA lightgray 0.5 1 NA -Inf
13 13 -0.86201518 1 13 NA lightgray 0.5 1 NA -Inf
14 14 -1.02975453 1 14 NA lightgray 0.5 1 NA -Inf
15 15 -0.52994744 1 15 NA lightgray 0.5 1 NA -Inf
16 16 -0.39153060 1 16 NA lightgray 0.5 1 NA -Inf
17 17 -1.26931872 1 17 NA lightgray 0.5 1 NA -Inf
18 18 -1.26931875 1 18 NA lightgray 0.5 1 NA -Inf
19 19 -1.10265208 1 19 NA lightgray 0.5 1 NA -Inf
20 20 -1.10265208 1 20 NA lightgray 0.5 1 NA -Inf
21 21 -1.10265208 1 21 NA lightgray 0.5 1 NA -Inf
22 22 -1.26931874 1 22 NA lightgray 0.5 1 NA -Inf
23 23 -1.26931920 1 23 NA lightgray 0.5 1 NA -Inf
24 24 -1.27395738 1 24 NA lightgray 0.5 1 NA -Inf
25 25 -1.27395738 1 25 NA lightgray 0.5 1 NA -Inf
26 26 -1.60729073 1 26 NA lightgray 0.5 1 NA -Inf
27 27 -1.60729073 1 27 NA lightgray 0.5 1 NA -Inf
28 28 -1.54656020 1 28 NA lightgray 0.5 1 NA -Inf
29 29 -1.54656020 1 29 NA lightgray 0.5 1 NA -Inf
30 30 -1.37256419 1 30 NA lightgray 0.5 1 NA -Inf
31 31 -1.37252079 1 31 NA lightgray 0.5 1 NA -Inf
32 32 -1.53918841 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
3 Inf 2.637704e-14 2.637802e-14
4 Inf 2.637704e-14 2.637802e-14
5 Inf 2.637704e-14 2.637802e-14
6 Inf 2.637704e-14 2.637802e-14
7 Inf 2.637704e-14 2.637802e-14
8 Inf 2.637704e-14 2.637802e-14
9 Inf 2.637704e-14 2.637802e-14
10 Inf 2.637704e-14 2.637802e-14
11 Inf 2.637704e-14 2.637802e-14
12 Inf 2.637704e-14 2.637802e-14
13 Inf 2.637704e-14 2.637802e-14
14 Inf 2.637704e-14 2.637802e-14
15 Inf 2.637704e-14 2.637802e-14
16 Inf 2.637704e-14 2.637802e-14
17 Inf 2.637704e-14 2.637802e-14
18 Inf 2.637704e-14 2.637802e-14
19 Inf 2.637704e-14 2.637802e-14
20 Inf 2.637704e-14 2.637802e-14
21 Inf 2.637704e-14 2.637802e-14
22 Inf 2.637704e-14 2.637802e-14
23 Inf 2.637704e-14 2.637802e-14
24 Inf 2.637704e-14 2.637802e-14
25 Inf 2.637704e-14 2.637802e-14
26 Inf 2.637704e-14 2.637802e-14
27 Inf 2.637704e-14 2.637802e-14
28 Inf 2.637704e-14 2.637802e-14
29 Inf 2.637704e-14 2.637802e-14
30 Inf 2.637704e-14 2.637802e-14
31 Inf 2.637704e-14 2.637802e-14
32 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$gear
x y PANEL group colour fill linewidth linetype alpha xmin xmax
1 1 0.12047749 1 1 NA lightgray 0.5 1 NA -Inf Inf
2 2 -0.24916998 1 2 NA lightgray 0.5 1 NA -Inf Inf
3 3 -0.04982882 1 3 NA lightgray 0.5 1 NA -Inf Inf
ymin ymax
1 2.637704e-14 2.637802e-14
2 2.637704e-14 2.637802e-14
3 2.637704e-14 2.637802e-14
$`North America`$d1$carb
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 1.076012e-07 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -3.593976e-08 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -3.594862e-08 1 -1 NA lightgray 0.5 1 NA -Inf
4 4 -3.594871e-08 1 -1 NA lightgray 0.5 1 NA -Inf
5 8 -4.130466e-08 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 2.637704e-14 2.637802e-14
2 Inf 2.637704e-14 2.637802e-14
3 Inf 2.637704e-14 2.637802e-14
4 Inf 2.637704e-14 2.637802e-14
5 Inf 2.637704e-14 2.637802e-14
$`North America`$d1$wt
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 6.601906e-10 1 -1 NA lightgray 0.5 1 NA
2 1.935000 2.636903e-10 1 -1 NA lightgray 0.5 1 NA
3 2.465000 2.186298e-10 1 -1 NA lightgray 0.5 1 NA
4 2.790481 2.186272e-10 1 -1 NA lightgray 0.5 1 NA
5 3.190000 2.186272e-10 1 -1 NA lightgray 0.5 1 NA
6 3.439356 2.193488e-10 1 -1 NA lightgray 0.5 1 NA
7 3.520000 2.194414e-10 1 -1 NA lightgray 0.5 1 NA
8 3.730000 2.194093e-10 1 -1 NA lightgray 0.5 1 NA
9 4.046066 2.194083e-10 1 -1 NA lightgray 0.5 1 NA
10 5.453272 2.194047e-10 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 2.637704e-14 2.637802e-14
2 -Inf Inf 2.637704e-14 2.637802e-14
3 -Inf Inf 2.637704e-14 2.637802e-14
4 -Inf Inf 2.637704e-14 2.637802e-14
5 -Inf Inf 2.637704e-14 2.637802e-14
6 -Inf Inf 2.637704e-14 2.637802e-14
7 -Inf Inf 2.637704e-14 2.637802e-14
8 -Inf Inf 2.637704e-14 2.637802e-14
9 -Inf Inf 2.637704e-14 2.637802e-14
10 -Inf Inf 2.637704e-14 2.637802e-14
$`North America`$d2
$`North America`$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`mpg:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
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284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
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305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
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308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
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314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
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317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
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323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
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336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
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338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
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341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
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354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
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387 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
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399 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
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414 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
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417 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
418 #FFBBBB 10 14 1 302 9.5 10.5 13.5 14.5 NA 0.1 1
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425 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
426 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
427 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
428 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
429 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
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431 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
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435 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
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437 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
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440 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
441 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
442 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
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444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
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446 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
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459 #FFBBBB 3 16 1 80 2.5 3.5 15.5 16.5 NA 0.1 1
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467 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
468 #FFBBBB 6 16 1 176 5.5 6.5 15.5 16.5 NA 0.1 1
469 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
470 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
471 #FFBBBB 7 16 1 208 6.5 7.5 15.5 16.5 NA 0.1 1
472 #FFBBBB 8 16 1 240 7.5 8.5 15.5 16.5 NA 0.1 1
473 #FFBBBB 8 16 1 240 7.5 8.5 15.5 16.5 NA 0.1 1
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476 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
477 #FFBBBB 9 16 1 272 8.5 9.5 15.5 16.5 NA 0.1 1
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492 #FFBBBB 4 17 1 113 3.5 4.5 16.5 17.5 NA 0.1 1
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497 #FFBBBB 6 17 1 177 5.5 6.5 16.5 17.5 NA 0.1 1
498 #FFBBBB 6 17 1 177 5.5 6.5 16.5 17.5 NA 0.1 1
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501 #FFBBBB 7 17 1 209 6.5 7.5 16.5 17.5 NA 0.1 1
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503 #FFBBBB 8 17 1 241 7.5 8.5 16.5 17.5 NA 0.1 1
504 #FFBBBB 8 17 1 241 7.5 8.5 16.5 17.5 NA 0.1 1
505 #FFBBBB 9 17 1 273 8.5 9.5 16.5 17.5 NA 0.1 1
506 #FFBBBB 9 17 1 273 8.5 9.5 16.5 17.5 NA 0.1 1
507 #FFBBBB 9 17 1 273 8.5 9.5 16.5 17.5 NA 0.1 1
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516 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
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522 #FFBBBB 4 18 1 114 3.5 4.5 17.5 18.5 NA 0.1 1
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525 #FFBBBB 5 18 1 146 4.5 5.5 17.5 18.5 NA 0.1 1
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527 #FFBBBB 6 18 1 178 5.5 6.5 17.5 18.5 NA 0.1 1
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529 #FFBBBB 7 18 1 210 6.5 7.5 17.5 18.5 NA 0.1 1
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531 #FFBBBB 7 18 1 210 6.5 7.5 17.5 18.5 NA 0.1 1
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535 #FFBBBB 9 18 1 274 8.5 9.5 17.5 18.5 NA 0.1 1
536 #FFBBBB 9 18 1 274 8.5 9.5 17.5 18.5 NA 0.1 1
537 #FFBBBB 9 18 1 274 8.5 9.5 17.5 18.5 NA 0.1 1
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871 NA NA NA
872 NA NA NA
873 NA NA NA
874 NA NA NA
875 NA NA NA
876 NA NA NA
877 NA NA NA
878 NA NA NA
879 NA NA NA
880 NA NA NA
881 NA NA NA
882 NA NA NA
883 NA NA NA
884 NA NA NA
885 NA NA NA
886 NA NA NA
887 NA NA NA
888 NA NA NA
889 NA NA NA
890 NA NA NA
891 NA NA NA
892 NA NA NA
893 NA NA NA
894 NA NA NA
895 NA NA NA
896 NA NA NA
897 NA NA NA
898 NA NA NA
899 NA NA NA
900 NA NA NA
901 NA NA NA
902 NA NA NA
903 NA NA NA
904 NA NA NA
905 NA NA NA
906 NA NA NA
907 NA NA NA
908 NA NA NA
909 NA NA NA
910 NA NA NA
911 NA NA NA
912 NA NA NA
913 NA NA NA
914 NA NA NA
915 NA NA NA
916 NA NA NA
917 NA NA NA
918 NA NA NA
919 NA NA NA
920 NA NA NA
921 NA NA NA
922 NA NA NA
923 NA NA NA
924 NA NA NA
925 NA NA NA
926 NA NA NA
927 NA NA NA
928 NA NA NA
929 NA NA NA
930 NA NA NA
931 NA NA NA
932 NA NA NA
933 NA NA NA
934 NA NA NA
935 NA NA NA
936 NA NA NA
937 NA NA NA
938 NA NA NA
939 NA NA NA
940 NA NA NA
941 NA NA NA
942 NA NA NA
943 NA NA NA
944 NA NA NA
945 NA NA NA
946 NA NA NA
947 NA NA NA
948 NA NA NA
949 NA NA NA
950 NA NA NA
951 NA NA NA
952 NA NA NA
953 NA NA NA
954 NA NA NA
955 NA NA NA
956 NA NA NA
957 NA NA NA
958 NA NA NA
959 NA NA NA
960 NA NA NA
$`North America`$d2$`mpg:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #FFBBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
11 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
12 #FFBBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1 NA
13 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
37 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
50 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
51 #FFBBBB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1 NA
52 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
53 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
54 #FFBBBB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1 NA
55 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
56 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
57 #FFBBBB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1 NA
58 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
59 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
60 #FFBBBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1 NA
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
64 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
65 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
66 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
67 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
68 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
69 #FFBBBB 3 3 1 9 2.5 3.5 2.5 3.5 NA 0.1 1 NA
70 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
71 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
72 #FFBBBB 4 3 1 12 3.5 4.5 2.5 3.5 NA 0.1 1 NA
73 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 5 3 1 15 4.5 5.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
77 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
78 #FFBBBB 6 3 1 18 5.5 6.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
80 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
81 #FFBBBB 7 3 1 21 6.5 7.5 2.5 3.5 NA 0.1 1 NA
82 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
83 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
84 #FFBBBB 8 3 1 24 7.5 8.5 2.5 3.5 NA 0.1 1 NA
85 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
86 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
87 #FFBBBB 9 3 1 27 8.5 9.5 2.5 3.5 NA 0.1 1 NA
88 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
89 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
90 #FFBBBB 10 3 1 30 9.5 10.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$`North America`$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$`North America`$d2$`mpg:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
289 NA NA NA
290 NA NA NA
291 NA NA NA
292 NA NA NA
293 NA NA NA
294 NA NA NA
295 NA NA NA
296 NA NA NA
297 NA NA NA
298 NA NA NA
299 NA NA NA
300 NA NA NA
$`North America`$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
$`North America`$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFBBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFBBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #FFBBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #FFBBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #FFBBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #FFBBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #FFBBBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #FFBBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #FFBBBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #FFBBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #FFBBBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #FFBBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFBBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #FFBBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #FFBBBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #FFBBBB 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #FFBBBB 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #FFBBBB 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #FFBBBB 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #FFBBBB 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #FFBBBB 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #FFBBBB 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #FFBBBB 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #FFBBBB 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #FFBBBB 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #FFBBBB 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #FFBBBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #FFBBBB 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #FFBBBB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #FFBBBB 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #FFBBBB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #FFBBBB 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$`North America`$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$`North America`$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$`North America`$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FF6D54 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFCCBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #A798FF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FF6D54 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFCCBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #A798FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FF6D54 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFCCBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #A798FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FF6D54 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFCCBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #A798FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FF6D54 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFCCBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #A798FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFCABB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFCCBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBCCFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FF6D54 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFCCBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #A99BFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFCBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFCCBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FF6B52 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFCCBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #A89AFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBFBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC4FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FF2512 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFCCBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #8D72FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBFBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFCCBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC4FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FF4F35 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFCCBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #9B86FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFC5BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFCCBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC9FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FF4F35 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFCCBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #9B86FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC5BB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #FFCCBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #BBC9FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FF6D54 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #FFCCBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #A798FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #FFCCBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FF6D54 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #FFCCBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #A798FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FF6D54 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFCCBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #A798FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFCBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FF6C54 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFCCBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #A798FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFCBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFCCBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #FFCCBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FF6C54 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFCCBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #A798FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFCBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFCCBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #FFCCBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FF634A 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFCBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #A99BFF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFCABB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFCCBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #BBCCFF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
100 #FF634A 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
101 #FFCBBB 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
102 #A99BFF 2 17 1 49 1.5 2.5 16.5 17.5 NA 0.1 1
103 #FFCBBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
104 #FFCCBB 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
105 #BBCCFF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
106 #FF6148 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
107 #FFCBBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
108 #A89AFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
109 #FFBFBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
110 #FFCCBB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
111 #BBC4FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
112 #FF0000 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
113 #FFCBBB 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
114 #8D72FF 2 19 1 51 1.5 2.5 18.5 19.5 NA 0.1 1
115 #FFC5BB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
116 #FFCCBB 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
117 #BBC9FF 1 20 1 20 0.5 1.5 19.5 20.5 NA 0.1 1
118 #FF422A 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
119 #FFCBBB 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
120 #9B86FF 2 20 1 52 1.5 2.5 19.5 20.5 NA 0.1 1
121 #FFC5BB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
122 #FFCCBB 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
123 #BBC9FF 1 21 1 21 0.5 1.5 20.5 21.5 NA 0.1 1
124 #FF422A 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
125 #FFCBBB 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
126 #9B86FF 2 21 1 53 1.5 2.5 20.5 21.5 NA 0.1 1
127 #FFCBBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
128 #FFCCBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
129 #FFCCBB 1 22 1 22 0.5 1.5 21.5 22.5 NA 0.1 1
130 #FF6349 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
131 #FFCBBB 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
132 #A798FF 2 22 1 54 1.5 2.5 21.5 22.5 NA 0.1 1
133 #FFCCBB 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
134 #BBC9FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
135 #BBC8FF 1 23 1 23 0.5 1.5 22.5 23.5 NA 0.1 1
136 #FF644B 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
137 #BBCAFF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
138 #9982FF 2 23 1 55 1.5 2.5 22.5 23.5 NA 0.1 1
139 #BBCCFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
140 #BBC3FF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
141 #BBBFFF 1 24 1 24 0.5 1.5 23.5 24.5 NA 0.1 1
142 #FF674E 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
143 #BBC4FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
144 #7556FF 2 24 1 56 1.5 2.5 23.5 24.5 NA 0.1 1
145 #BBCCFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
146 #BBC3FF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
147 #BBBFFF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
148 #FF674E 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
149 #BBC4FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
150 #7556FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
151 #BBCCFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
152 #BBC3FF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
153 #BBBFFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
154 #FF674E 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
155 #BBC4FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
156 #7556FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
157 #BBCCFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
158 #BBC3FF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
159 #BBBEFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
160 #FF684E 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
161 #BBC4FF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
162 #795AFF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
163 #BBCCFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
164 #BBC3FF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
165 #BBBEFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
166 #FF664C 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
167 #BBC4FF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
168 #7759FF 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
169 #FFCCBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
170 #BBC3FF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
171 #BBBEFF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
172 #FF1608 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
173 #BBC4FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
174 #472BFF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
175 #FFC0BB 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
176 #BBC3FF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
177 #B0A6FF 1 30 1 30 0.5 1.5 29.5 30.5 NA 0.1 1
178 #FF1608 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
179 #BBC4FF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
180 #472BFF 2 30 1 62 1.5 2.5 29.5 30.5 NA 0.1 1
181 #FFC6BB 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
182 #BBC3FF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
183 #BBBAFF 1 31 1 31 0.5 1.5 30.5 31.5 NA 0.1 1
184 #FF482F 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
185 #BBC4FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
186 #6344FF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
187 #FFC6BB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
188 #BBC3FF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
189 #BBBAFF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
190 #FF482F 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
191 #BBC1FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
192 #0000FF 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
$`North America`$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC4BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBC2FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF0000 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBC8FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #9F8CFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #B9B7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFA89F 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #B4ADFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #5134FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #BBC7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBC2FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #B9B7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFA9A2 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #AB9FFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #0000FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
$`North America`$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #D2D2D2 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF8A77 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #D2D2D2 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFB0AA 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #D2D2D2 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF8672 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #EFD9C4 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #D2D2D2 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #EFD9C4 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC2FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #DAD4CE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #D2D2D2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #D2D2D2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FF8A77 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #EFD9C3 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBC6FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFB0AA 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #D2D2D2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF0000 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #EFD9C3 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBC2FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
$`North America`$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBDDFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFA69C 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBDDFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FF9180 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBDDFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF0000 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #D2D2D2 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FF9180 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBDDFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #D2D2D2 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFA69C 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBEBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBDDFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FF0000 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #BBDDFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #BBCAFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FF9180 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
32 #BBDDFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
33 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
34 #FF0000 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
35 #BBDDFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
36 #BBCAFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
37 #FF9180 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
38 #BBDDFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
39 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
40 #FFA69C 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
41 #BBDDFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
42 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
43 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
44 #BBDDFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
45 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
46 #FF6F56 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
47 #BBDDFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
48 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
49 #FF9180 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
50 #BBDDFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
51 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
52 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
53 #BBDDFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
54 #BBCAFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
55 #FF9180 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
56 #BBDDFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
57 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
58 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
59 #BBDDFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
60 #BBCAFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
$`North America`$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFCBBB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
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27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
181 NA NA NA
182 NA NA NA
183 NA NA NA
184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
$`North America`$d2$`model:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCBFF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCBFF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC5BB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCBFF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC1FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FF9D91 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCBFF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #A99BFF 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FF8976 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCBFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #9E8BFF 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FF8470 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCBFF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #9C87FF 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FF826E 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCBFF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #9B85FF 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC3BB 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCBFF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC2FF 11 1 1 51 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC3BB 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCBFF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC2FF 12 1 1 56 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCBFF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC2FF 13 1 1 61 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBCBFF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC2FF 14 1 1 66 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBCBFF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC2FF 15 1 1 71 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC1BB 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBCBFF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC0FF 16 1 1 76 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FF9688 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBCBFF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
51 #A594FF 17 1 1 81 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FF6046 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBCBFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
54 #876BFF 18 1 1 86 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FF563C 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBCBFF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
57 #8265FF 19 1 1 91 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FF4D33 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBCBFF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
60 #7D5FFF 20 1 1 96 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FF4D33 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBCBFF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
63 #7D5FFF 21 1 1 101 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFC0BB 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBCBFF 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC0FF 22 1 1 106 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFC0BB 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBCBFF 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC0FF 23 1 1 111 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFC0BB 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBCBFF 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC0FF 24 1 1 116 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFC0BB 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBCBFF 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC0FF 25 1 1 121 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFC0BB 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBCBFF 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC0FF 26 1 1 126 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFB1AD 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBCBFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
81 #B3ACFF 27 1 1 131 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FF7962 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBCBFF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
84 #947CFF 28 1 1 136 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FF442B 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBCBFF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
87 #7859FF 29 1 1 141 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FF442B 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBCBFF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
90 #7859FF 30 1 1 146 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FF3922 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBCBFF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
93 #7353FF 31 1 1 151 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FF3922 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBCBFF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
96 #7353FF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #AFA5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCBFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
102 #AFA6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBCBFF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
105 #AFA6FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBCBFF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
108 #AFA6FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBCBFF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
111 #AFA6FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FF998B 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBCBFF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
114 #9279FF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FF482E 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBCBFF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
117 #583AFF 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FF200F 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBCBFF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
120 #3F25FF 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FF0D04 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBCBFF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
123 #371FFF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FF0000 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBCBFF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
126 #331CFF 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFBBBB 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBCBFF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
129 #AFA6FF 11 2 1 52 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFBBBB 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBCBFF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
132 #AFA6FF 12 2 1 57 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFBBBB 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBCBFF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
135 #AFA6FF 13 2 1 62 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFBCBB 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBCAFF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
138 #AFA6FF 14 2 1 67 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFBCBB 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBCAFF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
141 #AFA6FF 15 2 1 72 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFB6B4 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBCAFF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
144 #A99BFF 16 2 1 77 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FF7A63 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBCAFF 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
147 #7C5DFF 17 2 1 82 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FF3720 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBCAFF 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
150 #4C2FFF 18 2 1 87 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FF2714 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBCAFF 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
153 #4227FF 19 2 1 92 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FF1005 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
155 #BBCAFF 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
156 #371FFF 20 2 1 97 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FF1005 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
158 #BBCAFF 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
159 #371FFF 21 2 1 102 20.5 21.5 1.5 2.5 NA 0.1 1
160 #BBCBFF 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
161 #BBCAFF 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
162 #BBCAFF 22 2 1 107 21.5 22.5 1.5 2.5 NA 0.1 1
163 #BBCBFF 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
164 #BBCAFF 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBCAFF 23 2 1 112 22.5 23.5 1.5 2.5 NA 0.1 1
166 #BBCBFF 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
167 #BBCAFF 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBCAFF 24 2 1 117 23.5 24.5 1.5 2.5 NA 0.1 1
169 #BBCBFF 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
170 #BBCAFF 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
171 #BBCAFF 25 2 1 122 24.5 25.5 1.5 2.5 NA 0.1 1
172 #BBCBFF 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
173 #BBCAFF 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
174 #BBCAFF 26 2 1 127 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FFC8BB 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
176 #BBCAFF 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
177 #BBC3FF 27 2 1 132 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FFB4B1 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
179 #BBCAFF 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
180 #B4AEFF 28 2 1 137 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FF8C79 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
182 #BBCAFF 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
183 #9F8CFF 29 2 1 142 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF8B79 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
185 #BBCAFF 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
186 #9F8CFF 30 2 1 147 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF8571 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
188 #BBCAFF 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
189 #9C87FF 31 2 1 152 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FF8571 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
191 #BBCAFF 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
192 #9C87FF 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FFC3BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #BBC2FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FFC3BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCBFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
198 #BBC2FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FFC3BB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
200 #BBCBFF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
201 #BBC2FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FFC3BB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
203 #BBCBFF 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
204 #BBC2FF 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FFC3BB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBCBFF 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
207 #BBC2FF 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FFB6B3 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBCBFF 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
210 #B6B2FF 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FF6E56 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBCBFF 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
213 #8F75FF 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FF563C 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBCBFF 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
216 #8265FF 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FF5036 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBCBFF 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
219 #7F61FF 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FF4D33 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
221 #BBCBFF 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
222 #7D5FFF 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
223 #FFC0BB 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
224 #BBCBFF 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
225 #BBC0FF 11 3 1 53 10.5 11.5 2.5 3.5 NA 0.1 1
226 #FFC0BB 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
227 #BBCBFF 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
228 #BBC0FF 12 3 1 58 11.5 12.5 2.5 3.5 NA 0.1 1
229 #FFC0BB 13 3 1 63 12.5 13.5 2.5 3.5 NA 0.1 1
230 #BBCBFF 13 3 1 63 12.5 13.5 2.5 3.5 NA 0.1 1
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184 NA NA NA
185 NA NA NA
186 NA NA NA
187 NA NA NA
188 NA NA NA
189 NA NA NA
190 NA NA NA
191 NA NA NA
192 NA NA NA
193 NA NA NA
194 NA NA NA
195 NA NA NA
196 NA NA NA
197 NA NA NA
198 NA NA NA
199 NA NA NA
200 NA NA NA
201 NA NA NA
202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
289 NA NA NA
290 NA NA NA
291 NA NA NA
292 NA NA NA
293 NA NA NA
294 NA NA NA
295 NA NA NA
296 NA NA NA
297 NA NA NA
298 NA NA NA
299 NA NA NA
300 NA NA NA
301 NA NA NA
302 NA NA NA
303 NA NA NA
304 NA NA NA
305 NA NA NA
306 NA NA NA
307 NA NA NA
308 NA NA NA
309 NA NA NA
310 NA NA NA
311 NA NA NA
312 NA NA NA
313 NA NA NA
314 NA NA NA
315 NA NA NA
316 NA NA NA
317 NA NA NA
318 NA NA NA
319 NA NA NA
320 NA NA NA
321 NA NA NA
322 NA NA NA
323 NA NA NA
324 NA NA NA
325 NA NA NA
326 NA NA NA
327 NA NA NA
328 NA NA NA
329 NA NA NA
330 NA NA NA
331 NA NA NA
332 NA NA NA
333 NA NA NA
334 NA NA NA
335 NA NA NA
336 NA NA NA
337 NA NA NA
338 NA NA NA
339 NA NA NA
340 NA NA NA
341 NA NA NA
342 NA NA NA
343 NA NA NA
344 NA NA NA
345 NA NA NA
346 NA NA NA
347 NA NA NA
348 NA NA NA
349 NA NA NA
350 NA NA NA
351 NA NA NA
352 NA NA NA
353 NA NA NA
354 NA NA NA
355 NA NA NA
356 NA NA NA
357 NA NA NA
358 NA NA NA
359 NA NA NA
360 NA NA NA
361 NA NA NA
362 NA NA NA
363 NA NA NA
364 NA NA NA
365 NA NA NA
366 NA NA NA
367 NA NA NA
368 NA NA NA
369 NA NA NA
370 NA NA NA
371 NA NA NA
372 NA NA NA
373 NA NA NA
374 NA NA NA
375 NA NA NA
376 NA NA NA
377 NA NA NA
378 NA NA NA
379 NA NA NA
380 NA NA NA
381 NA NA NA
382 NA NA NA
383 NA NA NA
384 NA NA NA
385 NA NA NA
386 NA NA NA
387 NA NA NA
388 NA NA NA
389 NA NA NA
390 NA NA NA
391 NA NA NA
392 NA NA NA
393 NA NA NA
394 NA NA NA
395 NA NA NA
396 NA NA NA
397 NA NA NA
398 NA NA NA
399 NA NA NA
400 NA NA NA
401 NA NA NA
402 NA NA NA
403 NA NA NA
404 NA NA NA
405 NA NA NA
406 NA NA NA
407 NA NA NA
408 NA NA NA
409 NA NA NA
410 NA NA NA
411 NA NA NA
412 NA NA NA
413 NA NA NA
414 NA NA NA
415 NA NA NA
416 NA NA NA
417 NA NA NA
418 NA NA NA
419 NA NA NA
420 NA NA NA
421 NA NA NA
422 NA NA NA
423 NA NA NA
424 NA NA NA
425 NA NA NA
426 NA NA NA
427 NA NA NA
428 NA NA NA
429 NA NA NA
430 NA NA NA
431 NA NA NA
432 NA NA NA
433 NA NA NA
434 NA NA NA
435 NA NA NA
436 NA NA NA
437 NA NA NA
438 NA NA NA
439 NA NA NA
440 NA NA NA
441 NA NA NA
442 NA NA NA
443 NA NA NA
444 NA NA NA
445 NA NA NA
446 NA NA NA
447 NA NA NA
448 NA NA NA
449 NA NA NA
450 NA NA NA
451 NA NA NA
452 NA NA NA
453 NA NA NA
454 NA NA NA
455 NA NA NA
456 NA NA NA
457 NA NA NA
458 NA NA NA
459 NA NA NA
460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
$`North America`$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCAFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBCCFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCAFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBCBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCAFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBBEFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFB0AB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCAFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBBCFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFACA6 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCAFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBBCFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFACA6 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBCAFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBBCFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBCAFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBCAFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBCAFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBCAFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBCAFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBCBFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBCAFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC9FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #BBCCFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBCAFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC7FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFB1AC 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBCAFF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBBCFF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFAEA9 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBCAFF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBBCFF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFACA6 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBCAFF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBBCFF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFACA6 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBCAFF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBBCFF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #BBCAFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBCAFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBCAFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #BBCAFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBCAFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBCAFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
70 #BBCAFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBCAFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBCAFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #BBCAFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBCAFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBCAFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
76 #BBCAFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBCAFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBCAFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFCCBB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBCAFF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC6FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFC6BB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBCAFF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC3FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFB0AB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBCAFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBBCFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFADA7 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBCAFF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBBCFF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFABA4 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBCAFF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBBCFF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFABA4 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBCAFF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBBCFF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC9FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBCAFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC9FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBCAFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC9FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFCBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBCAFF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBCAFF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBBEFF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFADA7 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBCAFF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBBCFF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFA9A2 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBCAFF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBBBFF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFA9A2 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBCAFF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBBBFF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFA8A0 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBCAFF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BAB9FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFA8A0 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBCAFF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BAB9FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFA8A0 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBCAFF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BAB9FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFA8A0 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBCAFF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BAB9FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFA8A0 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBCAFF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BAB9FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFA89F 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBCAFF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
144 #B8B6FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FF998C 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBCAFF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
147 #B1A9FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FF3E26 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBCAFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
150 #937AFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FF3921 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBCAFF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
153 #9179FF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FF351E 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
155 #BBCAFF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
156 #9177FF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FF351E 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
158 #BBCAFF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
159 #9177FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FF9E93 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
161 #BBCAFF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
162 #B7B4FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FF9E93 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
164 #BBCAFF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
165 #B7B4FF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FF9E93 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
167 #BBCAFF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
168 #B7B4FF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FF9E93 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
170 #BBCAFF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
171 #B7B4FF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
172 #FF9E93 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
173 #BBCAFF 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
174 #B7B4FF 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
175 #FF907F 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
176 #BBCAFF 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
177 #AEA3FF 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
178 #FF7761 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
179 #BBCAFF 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
180 #A595FF 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
181 #FF2A16 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
182 #BBCAFF 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
183 #8E74FF 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
184 #FF2311 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
185 #BBCAFF 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
186 #8D73FF 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
187 #FF1D0D 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
188 #BBCAFF 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
189 #8C71FF 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
190 #FF1D0D 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
191 #BBCAFF 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
192 #8C71FF 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
193 #FF9A8C 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
194 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
195 #B5B1FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
196 #FF9A8C 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
197 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
198 #B5B1FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
199 #FF9A8C 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
200 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
201 #B5B1FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
202 #FF9A8C 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
203 #BBCAFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
204 #B5B1FF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
205 #FF9A8C 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
206 #BBCAFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
207 #B5B0FF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
208 #FF8B79 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
209 #BBCAFF 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
210 #ACA0FF 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
211 #FF3F27 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
212 #BBCAFF 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
213 #937BFF 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
214 #FF1A0B 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
215 #BBCAFF 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
216 #8C71FF 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
217 #FF0502 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
218 #BBCAFF 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
219 #8A6EFF 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
220 #FF0502 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
221 #BBCAFF 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
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958 NA NA NA
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$`North America`$d2$`gear:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC2BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC9BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBEBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC9BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBDBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFC9BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #BBC6FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFB2AF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #FFC9BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #BBC3FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #FF826D 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #FFC0BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #FFA69D 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #FFC0BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #BBC9FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #FF8976 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #FFBDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #FF745D 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #FFBDBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBC5FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FFB2AE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #FF593F 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #FF8A78 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #FF3A23 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #FF8976 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #FFC7BB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFBDBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #FFC7BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #FF1508 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #FF8B78 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #FFCCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #FF0000 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #FF8976 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #FFCCBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
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18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
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25 NA NA
26 NA NA
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31 NA NA
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34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
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40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
$`North America`$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFB3AF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC5BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFABA4 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC5BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFA69D 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #FFC5BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FF9B8E 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #FF7A64 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #FFC0BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #FF7A64 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #FFC0BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FF8875 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC9FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #FF735C 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #FF735C 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #FFBFBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FF8875 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #FFC2BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #FF8B79 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #FFCBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #FF836F 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #FFBABA 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #BBCCFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFA49B 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #FFC2BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #FF2613 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #FF7A64 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #FFC2BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #FF1709 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #FF7760 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #FFC6BB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FF8875 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
47 #FFC2BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
48 #BBC9FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1 NA
49 #FF2311 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
50 #FF7A64 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
51 #FFC6BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1 NA
52 #FF0B03 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
53 #FF765E 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
54 #FFC6BB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1 NA
55 #FF8875 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
56 #FFC2BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
57 #BBC9FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1 NA
58 #FF2311 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
59 #FF7A64 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
60 #FFC6BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1 NA
61 #FF1206 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
62 #FF755E 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
63 #FFBDBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1 NA
64 #FFAAA2 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
65 #FFC2BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
66 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1 NA
67 #FF2714 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
68 #FF7A64 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
69 #FFBFBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1 NA
70 #FF1608 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
71 #FF755E 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
72 #FFC2BB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1 NA
73 #FF8875 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
74 #FFC2BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
75 #BBC9FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1 NA
76 #FF2311 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
77 #FF7A64 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
78 #FFC6BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1 NA
79 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
80 #FF755E 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
81 #FFC6BB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1 NA
82 #FF8875 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
83 #FFC2BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
84 #BBC9FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1 NA
85 #FF2311 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
86 #FF7A64 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
87 #FFC6BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1 NA
88 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
89 #FF755E 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
90 #FFC6BB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$`North America`$d2$`carb:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FF5A40 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBDDFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBDDFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCCFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FF3C24 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBDDFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FF7760 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBDDFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FF3720 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBDDFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBCAFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FF0000 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
17 #BBDDFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
18 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
19 #FFC2BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
20 #BBDDFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
21 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
22 #FF0000 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
23 #BBDDFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
24 #BBCAFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FF644A 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBDDFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
27 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
28 #FF0000 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
29 #BBDDFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
30 #BBCAFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
31 #FF0000 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
32 #BBDDFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
33 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
34 #FFC2BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
35 #BBDDFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
36 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
37 #FF0000 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
38 #BBDDFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
39 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
40 #FF644A 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
41 #BBDDFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
42 #BBCBFF 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
43 #FF0000 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
44 #BBDDFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
45 #BBCAFF 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
46 #FF0000 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
47 #BBDDFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
48 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
49 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
50 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBDDFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #FF5A40 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
53 #BBDDFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
54 #BBCAFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
55 #FF7A63 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
56 #BBDDFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
57 #BBCBFF 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
58 #FF3C24 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
59 #BBDDFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
60 #BBCAFF 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
61 #FF3720 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
62 #BBDDFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
63 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
64 #FFC2BB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
65 #BBDDFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
66 #BBCCFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
67 #FF0000 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
68 #BBDDFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
69 #BBCAFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
70 #FF644A 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
71 #BBDDFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
72 #BBCBFF 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
73 #FF0000 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
74 #BBDDFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
75 #BBCAFF 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
76 #FF0000 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
77 #BBDDFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
78 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
79 #FFC2BB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
80 #BBDDFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
81 #BBCCFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
82 #FF0000 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
83 #BBDDFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
84 #BBCAFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
85 #FF644A 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
86 #BBDDFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
87 #BBCBFF 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
88 #FF0000 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
89 #BBDDFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
90 #BBCAFF 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
91 #FF0000 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
92 #BBDDFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
93 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
94 #FFC2BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
95 #BBDDFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
96 #BBCCFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
97 #FF0000 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
98 #BBDDFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
99 #BBCAFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
100 #FF8F7E 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
101 #BBDDFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
102 #BBCBFF 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
103 #FF5A40 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
104 #BBDDFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
105 #BBCAFF 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
106 #FF3C24 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
107 #BBDDFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
108 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
109 #FFC6BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
110 #BBDDFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
111 #BBCCFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
112 #FF3720 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
113 #BBDDFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
114 #BBCAFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
115 #FF644A 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
116 #BBDDFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
117 #BBCBFF 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
118 #FF0000 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
119 #BBDDFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
120 #BBCAFF 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
121 #FF0000 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
122 #BBDDFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
123 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
124 #FFC2BB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
125 #BBDDFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
126 #BBCCFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
127 #FF0000 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
128 #BBDDFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
129 #BBCAFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
130 #FF644A 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
131 #BBDDFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
132 #BBCBFF 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
133 #FF0000 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
134 #BBDDFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
135 #BBCAFF 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
136 #FF0000 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
137 #BBDDFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
138 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
139 #FFC2BB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
140 #BBDDFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
141 #BBCCFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
142 #FF0000 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
143 #BBDDFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
144 #BBCAFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
145 #FF644A 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
146 #BBDDFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
147 #BBCBFF 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
148 #FF0000 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
149 #BBDDFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
150 #BBCAFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
$`North America`$eff
$`North America`$eff[[1]]
y PANEL group colour fill linewidth linetype alpha xmin
1 1 1 1 NA lightgray 0.5 1 NA 2.637704e-14
2 2 1 2 NA lightgray 0.5 1 NA 2.637704e-14
3 3 1 3 NA lightgray 0.5 1 NA 2.637704e-14
4 4 1 4 NA lightgray 0.5 1 NA 2.637704e-14
5 5 1 5 NA lightgray 0.5 1 NA 2.637704e-14
6 6 1 6 NA lightgray 0.5 1 NA 2.637704e-14
7 7 1 7 NA lightgray 0.5 1 NA 2.637704e-14
xmax ymin ymax
1 2.637802e-14 -Inf Inf
2 2.637802e-14 -Inf Inf
3 2.637802e-14 -Inf Inf
4 2.637802e-14 -Inf Inf
5 2.637802e-14 -Inf Inf
6 2.637802e-14 -Inf Inf
7 2.637802e-14 -Inf Inf
$`North America`$eff[[2]]
xmin xmax y PANEL group ymin ymax colour linewidth
1 2.637774e-14 2.637774e-14 1 1 1 0.875 1.125 black 0.5
2 2.637774e-14 2.637774e-14 2 1 2 1.875 2.125 black 0.5
3 3.526728e-10 6.601906e-10 3 1 3 2.875 3.125 black 0.5
4 -9.118766e-10 3.461658e-10 4 1 4 3.875 4.125 black 0.5
5 -4.132864e-08 1.076012e-07 5 1 5 4.875 5.125 black 0.5
6 -2.322912e-01 6.023659e-02 6 1 6 5.875 6.125 black 0.5
7 -2.586368e+00 -1.112267e-01 7 1 7 6.875 7.125 black 0.5
linetype height alpha
1 1 0.25 NA
2 1 0.25 NA
3 1 0.25 NA
4 1 0.25 NA
5 1 0.25 NA
6 1 0.25 NA
7 1 0.25 NA
$`North America`$eff[[3]]
xmin xmax ymin ymax y PANEL group colour fill linewidth
1 2.637753e-14 2.637753e-14 0.7 1.3 1 1 1 NA white 0.5
2 2.637753e-14 2.637753e-14 1.7 2.3 2 1 2 NA white 0.5
3 -1.916743e-10 1.917271e-10 2.7 3.3 3 1 3 NA white 0.5
4 -3.505835e-10 3.506362e-10 3.7 4.3 4 1 4 NA white 0.5
5 -1.701125e-08 1.701130e-08 4.7 5.3 5 1 5 NA white 0.5
6 -6.394821e-02 6.394821e-02 5.7 6.3 6 1 6 NA white 0.5
7 -7.410985e-01 7.410985e-01 6.7 7.3 7 1 7 NA white 0.5
linetype alpha
1 1 NA
2 1 NA
3 1 NA
4 1 NA
5 1 NA
6 1 NA
7 1 NA
$`North America`$eff[[4]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 NALED 0.0% 1 1 1 black 3 0 0.5 -1 NA
2 2.637753e-14 NALED 0.0% 2 1 2 black 3 0 0.5 -1 NA
3 2.637753e-14 NALED 8.1% 3 1 3 black 3 0 0.5 -1 NA
4 2.637753e-14 NALED 41.7% 4 1 4 black 3 0 0.5 -1 NA
5 2.637753e-14 NALED 20.4% 5 1 5 black 3 0 0.5 -1 NA
6 2.637753e-14 NALED 29.5% 6 1 6 black 3 0 0.5 -1 NA
7 2.637753e-14 NALED 43.0% 7 1 7 black 3 0 0.5 -1 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[5]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ( 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 2.637753e-14 ( 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 -1.916743e-10 ( 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 -3.505835e-10 ( 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 -1.701125e-08 ( 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 -6.394821e-02 ( 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 -7.410985e-01 ( 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[6]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ) 1.02 1 1 black 3.88 0 0.5 0.5 NA
2 2.637753e-14 ) 2.02 1 2 black 3.88 0 0.5 0.5 NA
3 1.917271e-10 ) 3.02 1 3 black 3.88 0 0.5 0.5 NA
4 3.506362e-10 ) 4.02 1 4 black 3.88 0 0.5 0.5 NA
5 1.701130e-08 ) 5.02 1 5 black 3.88 0 0.5 0.5 NA
6 6.394821e-02 ) 6.02 1 6 black 3.88 0 0.5 0.5 NA
7 7.410985e-01 ) 7.02 1 7 black 3.88 0 0.5 0.5 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[7]]
x label y PANEL group colour size angle hjust vjust alpha
1 2.637753e-14 ALED 0.00 1 1 1 black 3 0 0.5 2 NA
2 2.637753e-14 ALED 0.00 2 1 2 black 3 0 0.5 2 NA
3 2.637753e-14 ALED 0.00 3 1 3 black 3 0 0.5 2 NA
4 2.637753e-14 ALED 0.00 4 1 4 black 3 0 0.5 2 NA
5 2.637753e-14 ALED 0.00 5 1 5 black 3 0 0.5 2 NA
6 2.637753e-14 ALED 0.13 6 1 6 black 3 0 0.5 2 NA
7 2.637753e-14 ALED 1.48 7 1 7 black 3 0 0.5 2 NA
family fontface lineheight
1 1 1.2
2 1 1.2
3 1 1.2
4 1 1.2
5 1 1.2
6 1 1.2
7 1 1.2
$`North America`$eff[[8]]
x y PANEL group colour fill size angle hjust vjust alpha family fontface
1 1 7 1 -1 black white 3 0 1 0.5 NA 1
lineheight label
1 1.2 Explanation of symbols:\n[N]ALER min |--( [N]ALED )--| [N]ALER max
$.all_cats
$.all_cats$d1
$.all_cats$d1$mpg
$.all_cats$d1$mpg$overlay
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
7 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
8 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
9 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
10 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
11 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
12 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
13 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
14 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
15 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
16 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
17 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
18 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
19 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
20 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
21 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
22 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
23 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
24 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
25 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
26 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
27 21.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
28 22.87332 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
29 30.10414 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
30 33.90000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 2.637802e-14
2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
4 -Inf Inf 1.197341e-14 2.637802e-14
5 -Inf Inf 1.197341e-14 2.637802e-14
6 -Inf Inf 1.197341e-14 2.637802e-14
7 -Inf Inf 1.197341e-14 2.637802e-14
8 -Inf Inf 1.197341e-14 2.637802e-14
9 -Inf Inf 1.197341e-14 2.637802e-14
10 -Inf Inf 1.197341e-14 2.637802e-14
11 -Inf Inf 1.197341e-14 2.637802e-14
12 -Inf Inf 1.197341e-14 2.637802e-14
13 -Inf Inf 1.197341e-14 2.637802e-14
14 -Inf Inf 1.197341e-14 2.637802e-14
15 -Inf Inf 1.197341e-14 2.637802e-14
16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$mpg$facet
x y PANEL group colour fill linewidth linetype alpha
1 10.37589 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
2 14.41394 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
3 15.20000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
4 16.40000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
5 18.70000 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
6 19.84566 1.305186e-14 1 -1 NA lightgray 0.5 1 NA
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91 27 -1.60729073 1 27 NA lightgray 0.5 1 NA -Inf
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93 29 -1.54656020 1 29 NA lightgray 0.5 1 NA -Inf
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95 31 -1.37252079 1 31 NA lightgray 0.5 1 NA -Inf
96 32 -1.53918841 1 32 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
10 Inf 1.197341e-14 2.637802e-14
11 Inf 1.197341e-14 2.637802e-14
12 Inf 1.197341e-14 2.637802e-14
13 Inf 1.197341e-14 2.637802e-14
14 Inf 1.197341e-14 2.637802e-14
15 Inf 1.197341e-14 2.637802e-14
16 Inf 1.197341e-14 2.637802e-14
17 Inf 1.197341e-14 2.637802e-14
18 Inf 1.197341e-14 2.637802e-14
19 Inf 1.197341e-14 2.637802e-14
20 Inf 1.197341e-14 2.637802e-14
21 Inf 1.197341e-14 2.637802e-14
22 Inf 1.197341e-14 2.637802e-14
23 Inf 1.197341e-14 2.637802e-14
24 Inf 1.197341e-14 2.637802e-14
25 Inf 1.197341e-14 2.637802e-14
26 Inf 1.197341e-14 2.637802e-14
27 Inf 1.197341e-14 2.637802e-14
28 Inf 1.197341e-14 2.637802e-14
29 Inf 1.197341e-14 2.637802e-14
30 Inf 1.197341e-14 2.637802e-14
31 Inf 1.197341e-14 2.637802e-14
32 Inf 1.197341e-14 2.637802e-14
33 Inf 1.197341e-14 2.637802e-14
34 Inf 1.197341e-14 2.637802e-14
35 Inf 1.197341e-14 2.637802e-14
36 Inf 1.197341e-14 2.637802e-14
37 Inf 1.197341e-14 2.637802e-14
38 Inf 1.197341e-14 2.637802e-14
39 Inf 1.197341e-14 2.637802e-14
40 Inf 1.197341e-14 2.637802e-14
41 Inf 1.197341e-14 2.637802e-14
42 Inf 1.197341e-14 2.637802e-14
43 Inf 1.197341e-14 2.637802e-14
44 Inf 1.197341e-14 2.637802e-14
45 Inf 1.197341e-14 2.637802e-14
46 Inf 1.197341e-14 2.637802e-14
47 Inf 1.197341e-14 2.637802e-14
48 Inf 1.197341e-14 2.637802e-14
49 Inf 1.197341e-14 2.637802e-14
50 Inf 1.197341e-14 2.637802e-14
51 Inf 1.197341e-14 2.637802e-14
52 Inf 1.197341e-14 2.637802e-14
53 Inf 1.197341e-14 2.637802e-14
54 Inf 1.197341e-14 2.637802e-14
55 Inf 1.197341e-14 2.637802e-14
56 Inf 1.197341e-14 2.637802e-14
57 Inf 1.197341e-14 2.637802e-14
58 Inf 1.197341e-14 2.637802e-14
59 Inf 1.197341e-14 2.637802e-14
60 Inf 1.197341e-14 2.637802e-14
61 Inf 1.197341e-14 2.637802e-14
62 Inf 1.197341e-14 2.637802e-14
63 Inf 1.197341e-14 2.637802e-14
64 Inf 1.197341e-14 2.637802e-14
65 Inf 1.197341e-14 2.637802e-14
66 Inf 1.197341e-14 2.637802e-14
67 Inf 1.197341e-14 2.637802e-14
68 Inf 1.197341e-14 2.637802e-14
69 Inf 1.197341e-14 2.637802e-14
70 Inf 1.197341e-14 2.637802e-14
71 Inf 1.197341e-14 2.637802e-14
72 Inf 1.197341e-14 2.637802e-14
73 Inf 1.197341e-14 2.637802e-14
74 Inf 1.197341e-14 2.637802e-14
75 Inf 1.197341e-14 2.637802e-14
76 Inf 1.197341e-14 2.637802e-14
77 Inf 1.197341e-14 2.637802e-14
78 Inf 1.197341e-14 2.637802e-14
79 Inf 1.197341e-14 2.637802e-14
80 Inf 1.197341e-14 2.637802e-14
81 Inf 1.197341e-14 2.637802e-14
82 Inf 1.197341e-14 2.637802e-14
83 Inf 1.197341e-14 2.637802e-14
84 Inf 1.197341e-14 2.637802e-14
85 Inf 1.197341e-14 2.637802e-14
86 Inf 1.197341e-14 2.637802e-14
87 Inf 1.197341e-14 2.637802e-14
88 Inf 1.197341e-14 2.637802e-14
89 Inf 1.197341e-14 2.637802e-14
90 Inf 1.197341e-14 2.637802e-14
91 Inf 1.197341e-14 2.637802e-14
92 Inf 1.197341e-14 2.637802e-14
93 Inf 1.197341e-14 2.637802e-14
94 Inf 1.197341e-14 2.637802e-14
95 Inf 1.197341e-14 2.637802e-14
96 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$gear
$.all_cats$d1$gear$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
3 3 -0.330118650 1 3 NA lightgray 0.5 1 NA -Inf
4 1 -0.238915351 1 1 NA lightgray 0.5 1 NA -Inf
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6 3 0.379947472 1 3 NA lightgray 0.5 1 NA -Inf
7 1 0.120477486 1 1 NA lightgray 0.5 1 NA -Inf
8 2 -0.249169983 1 2 NA lightgray 0.5 1 NA -Inf
9 3 -0.049828823 1 3 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$gear$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 0.118437865 1 1 NA lightgray 0.5 1 NA -Inf
2 2 0.001975945 1 2 NA lightgray 0.5 1 NA -Inf
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6 3 0.379947472 1 3 NA lightgray 0.5 1 NA -Inf
7 1 0.120477486 1 1 NA lightgray 0.5 1 NA -Inf
8 2 -0.249169983 1 2 NA lightgray 0.5 1 NA -Inf
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xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$carb
$.all_cats$d1$carb$overlay
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 5.489234e-03 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -1.898356e-03 1 -1 NA lightgray 0.5 1 NA -Inf
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13 3 -3.594863e-08 1 -1 NA lightgray 0.5 1 NA -Inf
14 4 -3.594873e-08 1 -1 NA lightgray 0.5 1 NA -Inf
15 8 -4.130468e-08 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
10 Inf 1.197341e-14 2.637802e-14
11 Inf 1.197341e-14 2.637802e-14
12 Inf 1.197341e-14 2.637802e-14
13 Inf 1.197341e-14 2.637802e-14
14 Inf 1.197341e-14 2.637802e-14
15 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$carb$facet
x y PANEL group colour fill linewidth linetype alpha xmin
1 1 5.489234e-03 1 -1 NA lightgray 0.5 1 NA -Inf
2 2 -1.898356e-03 1 -1 NA lightgray 0.5 1 NA -Inf
3 3 -1.898365e-03 1 -1 NA lightgray 0.5 1 NA -Inf
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7 2 1.898392e-03 1 -1 NA lightgray 0.5 1 NA -Inf
8 3 1.898401e-03 1 -1 NA lightgray 0.5 1 NA -Inf
9 4 1.898401e-03 1 -1 NA lightgray 0.5 1 NA -Inf
10 8 1.898406e-03 1 -1 NA lightgray 0.5 1 NA -Inf
11 1 1.076012e-07 1 -1 NA lightgray 0.5 1 NA -Inf
12 2 -3.593977e-08 1 -1 NA lightgray 0.5 1 NA -Inf
13 3 -3.594863e-08 1 -1 NA lightgray 0.5 1 NA -Inf
14 4 -3.594873e-08 1 -1 NA lightgray 0.5 1 NA -Inf
15 8 -4.130468e-08 1 -1 NA lightgray 0.5 1 NA -Inf
xmax ymin ymax
1 Inf 1.197341e-14 2.637802e-14
2 Inf 1.197341e-14 2.637802e-14
3 Inf 1.197341e-14 2.637802e-14
4 Inf 1.197341e-14 2.637802e-14
5 Inf 1.197341e-14 2.637802e-14
6 Inf 1.197341e-14 2.637802e-14
7 Inf 1.197341e-14 2.637802e-14
8 Inf 1.197341e-14 2.637802e-14
9 Inf 1.197341e-14 2.637802e-14
10 Inf 1.197341e-14 2.637802e-14
11 Inf 1.197341e-14 2.637802e-14
12 Inf 1.197341e-14 2.637802e-14
13 Inf 1.197341e-14 2.637802e-14
14 Inf 1.197341e-14 2.637802e-14
15 Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$wt
$.all_cats$d1$wt$overlay
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 3.193949e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 -1.434368e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 -9.061987e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
11 1.498275 -3.194015e-05 1 -1 NA lightgray 0.5 1 NA
12 1.935000 1.434104e-06 1 -1 NA lightgray 0.5 1 NA
13 2.465000 9.061769e-06 1 -1 NA lightgray 0.5 1 NA
14 2.790481 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
15 3.190000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
16 3.439356 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
17 3.520000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
18 3.730000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
19 4.046066 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
20 5.453272 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
21 1.498275 6.601773e-10 1 -1 NA lightgray 0.5 1 NA
22 1.935000 2.636769e-10 1 -1 NA lightgray 0.5 1 NA
23 2.465000 2.186165e-10 1 -1 NA lightgray 0.5 1 NA
24 2.790481 2.186139e-10 1 -1 NA lightgray 0.5 1 NA
25 3.190000 2.186139e-10 1 -1 NA lightgray 0.5 1 NA
26 3.439356 2.193354e-10 1 -1 NA lightgray 0.5 1 NA
27 3.520000 2.194281e-10 1 -1 NA lightgray 0.5 1 NA
28 3.730000 2.193960e-10 1 -1 NA lightgray 0.5 1 NA
29 4.046066 2.193949e-10 1 -1 NA lightgray 0.5 1 NA
30 5.453272 2.193914e-10 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 2.637802e-14
2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
4 -Inf Inf 1.197341e-14 2.637802e-14
5 -Inf Inf 1.197341e-14 2.637802e-14
6 -Inf Inf 1.197341e-14 2.637802e-14
7 -Inf Inf 1.197341e-14 2.637802e-14
8 -Inf Inf 1.197341e-14 2.637802e-14
9 -Inf Inf 1.197341e-14 2.637802e-14
10 -Inf Inf 1.197341e-14 2.637802e-14
11 -Inf Inf 1.197341e-14 2.637802e-14
12 -Inf Inf 1.197341e-14 2.637802e-14
13 -Inf Inf 1.197341e-14 2.637802e-14
14 -Inf Inf 1.197341e-14 2.637802e-14
15 -Inf Inf 1.197341e-14 2.637802e-14
16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d1$wt$facet
x y PANEL group colour fill linewidth linetype alpha
1 1.498275 3.193949e-05 1 -1 NA lightgray 0.5 1 NA
2 1.935000 -1.434368e-06 1 -1 NA lightgray 0.5 1 NA
3 2.465000 -9.061987e-06 1 -1 NA lightgray 0.5 1 NA
4 2.790481 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
5 3.190000 -9.072039e-06 1 -1 NA lightgray 0.5 1 NA
6 3.439356 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
7 3.520000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
8 3.730000 -9.072040e-06 1 -1 NA lightgray 0.5 1 NA
9 4.046066 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
10 5.453272 -9.072044e-06 1 -1 NA lightgray 0.5 1 NA
11 1.498275 -3.194015e-05 1 -1 NA lightgray 0.5 1 NA
12 1.935000 1.434104e-06 1 -1 NA lightgray 0.5 1 NA
13 2.465000 9.061769e-06 1 -1 NA lightgray 0.5 1 NA
14 2.790481 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
15 3.190000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
16 3.439356 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
17 3.520000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
18 3.730000 9.071821e-06 1 -1 NA lightgray 0.5 1 NA
19 4.046066 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
20 5.453272 9.071824e-06 1 -1 NA lightgray 0.5 1 NA
21 1.498275 6.601773e-10 1 -1 NA lightgray 0.5 1 NA
22 1.935000 2.636769e-10 1 -1 NA lightgray 0.5 1 NA
23 2.465000 2.186165e-10 1 -1 NA lightgray 0.5 1 NA
24 2.790481 2.186139e-10 1 -1 NA lightgray 0.5 1 NA
25 3.190000 2.186139e-10 1 -1 NA lightgray 0.5 1 NA
26 3.439356 2.193354e-10 1 -1 NA lightgray 0.5 1 NA
27 3.520000 2.194281e-10 1 -1 NA lightgray 0.5 1 NA
28 3.730000 2.193960e-10 1 -1 NA lightgray 0.5 1 NA
29 4.046066 2.193949e-10 1 -1 NA lightgray 0.5 1 NA
30 5.453272 2.193914e-10 1 -1 NA lightgray 0.5 1 NA
xmin xmax ymin ymax
1 -Inf Inf 1.197341e-14 2.637802e-14
2 -Inf Inf 1.197341e-14 2.637802e-14
3 -Inf Inf 1.197341e-14 2.637802e-14
4 -Inf Inf 1.197341e-14 2.637802e-14
5 -Inf Inf 1.197341e-14 2.637802e-14
6 -Inf Inf 1.197341e-14 2.637802e-14
7 -Inf Inf 1.197341e-14 2.637802e-14
8 -Inf Inf 1.197341e-14 2.637802e-14
9 -Inf Inf 1.197341e-14 2.637802e-14
10 -Inf Inf 1.197341e-14 2.637802e-14
11 -Inf Inf 1.197341e-14 2.637802e-14
12 -Inf Inf 1.197341e-14 2.637802e-14
13 -Inf Inf 1.197341e-14 2.637802e-14
14 -Inf Inf 1.197341e-14 2.637802e-14
15 -Inf Inf 1.197341e-14 2.637802e-14
16 -Inf Inf 1.197341e-14 2.637802e-14
17 -Inf Inf 1.197341e-14 2.637802e-14
18 -Inf Inf 1.197341e-14 2.637802e-14
19 -Inf Inf 1.197341e-14 2.637802e-14
20 -Inf Inf 1.197341e-14 2.637802e-14
21 -Inf Inf 1.197341e-14 2.637802e-14
22 -Inf Inf 1.197341e-14 2.637802e-14
23 -Inf Inf 1.197341e-14 2.637802e-14
24 -Inf Inf 1.197341e-14 2.637802e-14
25 -Inf Inf 1.197341e-14 2.637802e-14
26 -Inf Inf 1.197341e-14 2.637802e-14
27 -Inf Inf 1.197341e-14 2.637802e-14
28 -Inf Inf 1.197341e-14 2.637802e-14
29 -Inf Inf 1.197341e-14 2.637802e-14
30 -Inf Inf 1.197341e-14 2.637802e-14
$.all_cats$d2
$.all_cats$d2$`mpg:vs`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
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101 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
102 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
103 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
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105 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
106 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
107 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
108 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
109 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
110 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
111 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
112 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
113 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
114 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
115 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
116 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
117 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
118 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
119 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
120 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
121 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
122 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
123 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
124 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
125 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
126 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1
127 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
128 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
129 #FFBBBB 3 1 1 5 2.5 3.5 0.5 1.5 NA 0.1 1
130 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
131 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
132 #FFBBBB 4 1 1 7 3.5 4.5 0.5 1.5 NA 0.1 1
133 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
134 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
135 #FFBBBB 5 1 1 9 4.5 5.5 0.5 1.5 NA 0.1 1
136 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
137 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
138 #FFBBBB 6 1 1 11 5.5 6.5 0.5 1.5 NA 0.1 1
139 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
140 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
141 #FFBBBB 7 1 1 13 6.5 7.5 0.5 1.5 NA 0.1 1
142 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
143 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
144 #FFBBBB 8 1 1 15 7.5 8.5 0.5 1.5 NA 0.1 1
145 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
146 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
147 #FFBBBB 9 1 1 17 8.5 9.5 0.5 1.5 NA 0.1 1
148 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
149 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
150 #FFBBBB 10 1 1 19 9.5 10.5 0.5 1.5 NA 0.1 1
151 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
152 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
153 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
154 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
155 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
156 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1
157 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
158 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
159 #FFBBBB 3 2 1 6 2.5 3.5 1.5 2.5 NA 0.1 1
160 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
161 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
162 #FFBBBB 4 2 1 8 3.5 4.5 1.5 2.5 NA 0.1 1
163 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
164 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
165 #FFBBBB 5 2 1 10 4.5 5.5 1.5 2.5 NA 0.1 1
166 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
167 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
168 #FFBBBB 6 2 1 12 5.5 6.5 1.5 2.5 NA 0.1 1
169 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
170 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
171 #FFBBBB 7 2 1 14 6.5 7.5 1.5 2.5 NA 0.1 1
172 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
173 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
174 #FFBBBB 8 2 1 16 7.5 8.5 1.5 2.5 NA 0.1 1
175 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
176 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
177 #FFBBBB 9 2 1 18 8.5 9.5 1.5 2.5 NA 0.1 1
178 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
179 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
180 #FFBBBB 10 2 1 20 9.5 10.5 1.5 2.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
$.all_cats$d2$`mpg:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
160 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
161 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
162 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
163 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
164 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
165 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
166 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
167 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
169 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
170 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
172 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
173 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
174 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
175 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
176 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
177 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
178 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
179 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
180 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
181 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
182 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
183 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
184 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
185 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
186 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
187 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
188 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
189 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
190 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
191 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
192 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
193 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
194 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
195 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
196 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
197 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
198 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
199 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
200 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
201 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
202 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
203 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
204 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
205 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
206 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
207 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
208 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
209 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
210 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
211 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
212 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
213 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
214 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
215 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
216 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
217 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
218 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
220 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
221 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
222 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
223 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
224 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
225 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
226 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
227 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
228 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
229 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
230 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
231 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
232 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
233 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
234 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
235 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
236 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
238 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
239 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
240 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
241 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
242 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
243 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
244 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
245 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
246 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
247 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
248 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
249 #FFBBBB 3 9 1 73 2.5 3.5 8.5 9.5 NA 0.1 1
250 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
251 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
252 #FFBBBB 4 9 1 105 3.5 4.5 8.5 9.5 NA 0.1 1
253 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
254 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
255 #FFBBBB 5 9 1 137 4.5 5.5 8.5 9.5 NA 0.1 1
256 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
257 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
258 #FFBBBB 6 9 1 169 5.5 6.5 8.5 9.5 NA 0.1 1
259 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
260 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
261 #FFBBBB 7 9 1 201 6.5 7.5 8.5 9.5 NA 0.1 1
262 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
263 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
264 #FFBBBB 8 9 1 233 7.5 8.5 8.5 9.5 NA 0.1 1
265 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
266 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
267 #FFBBBB 9 9 1 265 8.5 9.5 8.5 9.5 NA 0.1 1
268 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
269 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
270 #FFBBBB 10 9 1 297 9.5 10.5 8.5 9.5 NA 0.1 1
271 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
272 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
273 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
274 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
275 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
276 #FFBBBB 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
277 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
278 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
279 #FFBBBB 3 10 1 74 2.5 3.5 9.5 10.5 NA 0.1 1
280 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
281 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
282 #FFBBBB 4 10 1 106 3.5 4.5 9.5 10.5 NA 0.1 1
283 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
284 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
285 #FFBBBB 5 10 1 138 4.5 5.5 9.5 10.5 NA 0.1 1
286 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
287 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
288 #FFBBBB 6 10 1 170 5.5 6.5 9.5 10.5 NA 0.1 1
289 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
290 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
291 #FFBBBB 7 10 1 202 6.5 7.5 9.5 10.5 NA 0.1 1
292 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
293 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 234 7.5 8.5 9.5 10.5 NA 0.1 1
295 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
296 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
297 #FFBBBB 9 10 1 266 8.5 9.5 9.5 10.5 NA 0.1 1
298 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
299 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
300 #FFBBBB 10 10 1 298 9.5 10.5 9.5 10.5 NA 0.1 1
301 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
302 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
303 #FFBBBB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
304 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
305 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
306 #FFBBBB 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
307 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
308 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
309 #FFBBBB 3 11 1 75 2.5 3.5 10.5 11.5 NA 0.1 1
310 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
311 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
312 #FFBBBB 4 11 1 107 3.5 4.5 10.5 11.5 NA 0.1 1
313 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
314 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
315 #FFBBBB 5 11 1 139 4.5 5.5 10.5 11.5 NA 0.1 1
316 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
317 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
318 #FFBBBB 6 11 1 171 5.5 6.5 10.5 11.5 NA 0.1 1
319 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
320 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
321 #FFBBBB 7 11 1 203 6.5 7.5 10.5 11.5 NA 0.1 1
322 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
323 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
324 #FFBBBB 8 11 1 235 7.5 8.5 10.5 11.5 NA 0.1 1
325 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
326 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
327 #FFBBBB 9 11 1 267 8.5 9.5 10.5 11.5 NA 0.1 1
328 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
329 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
330 #FFBBBB 10 11 1 299 9.5 10.5 10.5 11.5 NA 0.1 1
331 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
332 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
333 #FFBBBB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
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335 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
336 #FFBBBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
337 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
338 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
339 #FFBBBB 3 12 1 76 2.5 3.5 11.5 12.5 NA 0.1 1
340 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
341 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
342 #FFBBBB 4 12 1 108 3.5 4.5 11.5 12.5 NA 0.1 1
343 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
344 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
345 #FFBBBB 5 12 1 140 4.5 5.5 11.5 12.5 NA 0.1 1
346 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
347 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
348 #FFBBBB 6 12 1 172 5.5 6.5 11.5 12.5 NA 0.1 1
349 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
350 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
351 #FFBBBB 7 12 1 204 6.5 7.5 11.5 12.5 NA 0.1 1
352 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
353 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
354 #FFBBBB 8 12 1 236 7.5 8.5 11.5 12.5 NA 0.1 1
355 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
356 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
357 #FFBBBB 9 12 1 268 8.5 9.5 11.5 12.5 NA 0.1 1
358 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
359 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
360 #FFBBBB 10 12 1 300 9.5 10.5 11.5 12.5 NA 0.1 1
361 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
362 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
363 #FFBBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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365 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
366 #FFBBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
367 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
368 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
369 #FFBBBB 3 13 1 77 2.5 3.5 12.5 13.5 NA 0.1 1
370 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
371 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
372 #FFBBBB 4 13 1 109 3.5 4.5 12.5 13.5 NA 0.1 1
373 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
374 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
375 #FFBBBB 5 13 1 141 4.5 5.5 12.5 13.5 NA 0.1 1
376 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
377 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
378 #FFBBBB 6 13 1 173 5.5 6.5 12.5 13.5 NA 0.1 1
379 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
380 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
381 #FFBBBB 7 13 1 205 6.5 7.5 12.5 13.5 NA 0.1 1
382 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
383 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
384 #FFBBBB 8 13 1 237 7.5 8.5 12.5 13.5 NA 0.1 1
385 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
386 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
387 #FFBBBB 9 13 1 269 8.5 9.5 12.5 13.5 NA 0.1 1
388 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
389 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
390 #FFBBBB 10 13 1 301 9.5 10.5 12.5 13.5 NA 0.1 1
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393 #FFBBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
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395 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
396 #FFBBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
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398 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
399 #FFBBBB 3 14 1 78 2.5 3.5 13.5 14.5 NA 0.1 1
400 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
401 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
402 #FFBBBB 4 14 1 110 3.5 4.5 13.5 14.5 NA 0.1 1
403 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
404 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
405 #FFBBBB 5 14 1 142 4.5 5.5 13.5 14.5 NA 0.1 1
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407 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
408 #FFBBBB 6 14 1 174 5.5 6.5 13.5 14.5 NA 0.1 1
409 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
410 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
411 #FFBBBB 7 14 1 206 6.5 7.5 13.5 14.5 NA 0.1 1
412 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
413 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
414 #FFBBBB 8 14 1 238 7.5 8.5 13.5 14.5 NA 0.1 1
415 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
416 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
417 #FFBBBB 9 14 1 270 8.5 9.5 13.5 14.5 NA 0.1 1
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426 #FFBBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
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428 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
429 #FFBBBB 3 15 1 79 2.5 3.5 14.5 15.5 NA 0.1 1
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431 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
432 #FFBBBB 4 15 1 111 3.5 4.5 14.5 15.5 NA 0.1 1
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434 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
435 #FFBBBB 5 15 1 143 4.5 5.5 14.5 15.5 NA 0.1 1
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437 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
438 #FFBBBB 6 15 1 175 5.5 6.5 14.5 15.5 NA 0.1 1
439 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
440 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
441 #FFBBBB 7 15 1 207 6.5 7.5 14.5 15.5 NA 0.1 1
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443 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
444 #FFBBBB 8 15 1 239 7.5 8.5 14.5 15.5 NA 0.1 1
445 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
446 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
447 #FFBBBB 9 15 1 271 8.5 9.5 14.5 15.5 NA 0.1 1
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449 #FFBBBB 10 15 1 303 9.5 10.5 14.5 15.5 NA 0.1 1
450 #FFBBBB 10 15 1 303 9.5 10.5 14.5 15.5 NA 0.1 1
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455 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
456 #FFBBBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
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459 #FFBBBB 3 16 1 80 2.5 3.5 15.5 16.5 NA 0.1 1
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461 #FFBBBB 4 16 1 112 3.5 4.5 15.5 16.5 NA 0.1 1
462 #FFBBBB 4 16 1 112 3.5 4.5 15.5 16.5 NA 0.1 1
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464 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
465 #FFBBBB 5 16 1 144 4.5 5.5 15.5 16.5 NA 0.1 1
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1882 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1883 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1884 #FFBBBB 8 31 1 255 7.5 8.5 30.5 31.5 NA 0.1 1
1885 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1886 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1887 #FFBBBB 9 31 1 287 8.5 9.5 30.5 31.5 NA 0.1 1
1888 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1889 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1890 #FFBBBB 10 31 1 319 9.5 10.5 30.5 31.5 NA 0.1 1
1891 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1892 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1893 #FFBBBB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
1894 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1895 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1896 #FFBBBB 2 32 1 64 1.5 2.5 31.5 32.5 NA 0.1 1
1897 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1898 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1899 #FFBBBB 3 32 1 96 2.5 3.5 31.5 32.5 NA 0.1 1
1900 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1901 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1902 #FFBBBB 4 32 1 128 3.5 4.5 31.5 32.5 NA 0.1 1
1903 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1904 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1905 #FFBBBB 5 32 1 160 4.5 5.5 31.5 32.5 NA 0.1 1
1906 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1907 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1908 #FFBBBB 6 32 1 192 5.5 6.5 31.5 32.5 NA 0.1 1
1909 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1910 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1911 #FFBBBB 7 32 1 224 6.5 7.5 31.5 32.5 NA 0.1 1
1912 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1913 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1914 #FFBBBB 8 32 1 256 7.5 8.5 31.5 32.5 NA 0.1 1
1915 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1916 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1917 #FFBBBB 9 32 1 288 8.5 9.5 31.5 32.5 NA 0.1 1
1918 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1919 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1920 #FFBBBB 10 32 1 320 9.5 10.5 31.5 32.5 NA 0.1 1
1921 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1922 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1923 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1924 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1925 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1926 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
1927 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1928 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1929 #FFBBBB 3 1 1 65 2.5 3.5 0.5 1.5 NA 0.1 1
1930 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1931 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1932 #FFBBBB 4 1 1 97 3.5 4.5 0.5 1.5 NA 0.1 1
1933 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1934 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1935 #FFBBBB 5 1 1 129 4.5 5.5 0.5 1.5 NA 0.1 1
1936 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1937 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1938 #FFBBBB 6 1 1 161 5.5 6.5 0.5 1.5 NA 0.1 1
1939 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1940 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1941 #FFBBBB 7 1 1 193 6.5 7.5 0.5 1.5 NA 0.1 1
1942 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1943 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1944 #FFBBBB 8 1 1 225 7.5 8.5 0.5 1.5 NA 0.1 1
1945 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1946 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1947 #FFBBBB 9 1 1 257 8.5 9.5 0.5 1.5 NA 0.1 1
1948 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1949 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1950 #FFBBBB 10 1 1 289 9.5 10.5 0.5 1.5 NA 0.1 1
1951 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1952 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1953 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
1954 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1955 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1956 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
1957 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1958 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1959 #FFBBBB 3 2 1 66 2.5 3.5 1.5 2.5 NA 0.1 1
1960 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1961 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1962 #FFBBBB 4 2 1 98 3.5 4.5 1.5 2.5 NA 0.1 1
1963 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1964 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1965 #FFBBBB 5 2 1 130 4.5 5.5 1.5 2.5 NA 0.1 1
1966 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1967 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1968 #FFBBBB 6 2 1 162 5.5 6.5 1.5 2.5 NA 0.1 1
1969 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1970 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1971 #FFBBBB 7 2 1 194 6.5 7.5 1.5 2.5 NA 0.1 1
1972 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1973 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1974 #FFBBBB 8 2 1 226 7.5 8.5 1.5 2.5 NA 0.1 1
1975 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1976 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1977 #FFBBBB 9 2 1 258 8.5 9.5 1.5 2.5 NA 0.1 1
1978 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1979 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1980 #FFBBBB 10 2 1 290 9.5 10.5 1.5 2.5 NA 0.1 1
1981 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1982 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1983 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
1984 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1985 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1986 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
1987 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1988 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1989 #FFBBBB 3 3 1 67 2.5 3.5 2.5 3.5 NA 0.1 1
1990 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1991 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1992 #FFBBBB 4 3 1 99 3.5 4.5 2.5 3.5 NA 0.1 1
1993 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1994 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1995 #FFBBBB 5 3 1 131 4.5 5.5 2.5 3.5 NA 0.1 1
1996 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1997 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1998 #FFBBBB 6 3 1 163 5.5 6.5 2.5 3.5 NA 0.1 1
1999 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2000 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2001 #FFBBBB 7 3 1 195 6.5 7.5 2.5 3.5 NA 0.1 1
2002 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2003 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2004 #FFBBBB 8 3 1 227 7.5 8.5 2.5 3.5 NA 0.1 1
2005 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2006 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2007 #FFBBBB 9 3 1 259 8.5 9.5 2.5 3.5 NA 0.1 1
2008 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2009 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2010 #FFBBBB 10 3 1 291 9.5 10.5 2.5 3.5 NA 0.1 1
2011 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2012 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2013 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
2014 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2015 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2016 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
2017 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2018 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2019 #FFBBBB 3 4 1 68 2.5 3.5 3.5 4.5 NA 0.1 1
2020 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2021 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2022 #FFBBBB 4 4 1 100 3.5 4.5 3.5 4.5 NA 0.1 1
2023 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2024 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2025 #FFBBBB 5 4 1 132 4.5 5.5 3.5 4.5 NA 0.1 1
2026 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2027 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2028 #FFBBBB 6 4 1 164 5.5 6.5 3.5 4.5 NA 0.1 1
2029 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2030 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2031 #FFBBBB 7 4 1 196 6.5 7.5 3.5 4.5 NA 0.1 1
2032 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2033 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2034 #FFBBBB 8 4 1 228 7.5 8.5 3.5 4.5 NA 0.1 1
2035 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2036 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2037 #FFBBBB 9 4 1 260 8.5 9.5 3.5 4.5 NA 0.1 1
2038 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2039 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2040 #FFBBBB 10 4 1 292 9.5 10.5 3.5 4.5 NA 0.1 1
2041 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2042 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2043 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
2044 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2045 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2046 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
2047 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2048 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2049 #FFBBBB 3 5 1 69 2.5 3.5 4.5 5.5 NA 0.1 1
2050 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2051 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2052 #FFBBBB 4 5 1 101 3.5 4.5 4.5 5.5 NA 0.1 1
2053 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2054 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2055 #FFBBBB 5 5 1 133 4.5 5.5 4.5 5.5 NA 0.1 1
2056 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2057 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2058 #FFBBBB 6 5 1 165 5.5 6.5 4.5 5.5 NA 0.1 1
2059 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2060 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2061 #FFBBBB 7 5 1 197 6.5 7.5 4.5 5.5 NA 0.1 1
2062 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2063 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2064 #FFBBBB 8 5 1 229 7.5 8.5 4.5 5.5 NA 0.1 1
2065 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2066 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2067 #FFBBBB 9 5 1 261 8.5 9.5 4.5 5.5 NA 0.1 1
2068 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2069 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2070 #FFBBBB 10 5 1 293 9.5 10.5 4.5 5.5 NA 0.1 1
2071 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2072 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2073 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
2074 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2075 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2076 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
2077 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2078 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2079 #FFBBBB 3 6 1 70 2.5 3.5 5.5 6.5 NA 0.1 1
2080 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2081 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2082 #FFBBBB 4 6 1 102 3.5 4.5 5.5 6.5 NA 0.1 1
2083 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2084 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2085 #FFBBBB 5 6 1 134 4.5 5.5 5.5 6.5 NA 0.1 1
2086 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2087 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2088 #FFBBBB 6 6 1 166 5.5 6.5 5.5 6.5 NA 0.1 1
2089 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2090 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2091 #FFBBBB 7 6 1 198 6.5 7.5 5.5 6.5 NA 0.1 1
2092 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2093 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2094 #FFBBBB 8 6 1 230 7.5 8.5 5.5 6.5 NA 0.1 1
2095 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2096 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2097 #FFBBBB 9 6 1 262 8.5 9.5 5.5 6.5 NA 0.1 1
2098 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2099 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2100 #FFBBBB 10 6 1 294 9.5 10.5 5.5 6.5 NA 0.1 1
2101 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2102 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2103 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
2104 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2105 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2106 #FFBBBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
2107 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2108 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2109 #FFBBBB 3 7 1 71 2.5 3.5 6.5 7.5 NA 0.1 1
2110 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2111 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2112 #FFBBBB 4 7 1 103 3.5 4.5 6.5 7.5 NA 0.1 1
2113 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2114 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2115 #FFBBBB 5 7 1 135 4.5 5.5 6.5 7.5 NA 0.1 1
2116 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2117 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2118 #FFBBBB 6 7 1 167 5.5 6.5 6.5 7.5 NA 0.1 1
2119 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2120 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2121 #FFBBBB 7 7 1 199 6.5 7.5 6.5 7.5 NA 0.1 1
2122 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2123 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2124 #FFBBBB 8 7 1 231 7.5 8.5 6.5 7.5 NA 0.1 1
2125 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2126 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2127 #FFBBBB 9 7 1 263 8.5 9.5 6.5 7.5 NA 0.1 1
2128 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2129 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2130 #FFBBBB 10 7 1 295 9.5 10.5 6.5 7.5 NA 0.1 1
2131 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2132 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2133 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
2134 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2135 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2136 #FFBBBB 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
2137 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2138 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2139 #FFBBBB 3 8 1 72 2.5 3.5 7.5 8.5 NA 0.1 1
2140 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2141 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2142 #FFBBBB 4 8 1 104 3.5 4.5 7.5 8.5 NA 0.1 1
2143 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2144 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2145 #FFBBBB 5 8 1 136 4.5 5.5 7.5 8.5 NA 0.1 1
2146 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2147 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2148 #FFBBBB 6 8 1 168 5.5 6.5 7.5 8.5 NA 0.1 1
2149 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2150 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2151 #FFBBBB 7 8 1 200 6.5 7.5 7.5 8.5 NA 0.1 1
2152 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2153 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2154 #FFBBBB 8 8 1 232 7.5 8.5 7.5 8.5 NA 0.1 1
2155 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2156 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2157 #FFBBBB 9 8 1 264 8.5 9.5 7.5 8.5 NA 0.1 1
2158 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2159 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2160 #FFBBBB 10 8 1 296 9.5 10.5 7.5 8.5 NA 0.1 1
2161 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2162 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2163 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
2164 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
2165 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
2166 #FFBBBB 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
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fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
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226 NA NA NA
227 NA NA NA
228 NA NA NA
229 NA NA NA
230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
241 NA NA NA
242 NA NA NA
243 NA NA NA
244 NA NA NA
245 NA NA NA
246 NA NA NA
247 NA NA NA
248 NA NA NA
249 NA NA NA
250 NA NA NA
251 NA NA NA
252 NA NA NA
253 NA NA NA
254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
$.all_cats$d2$`mpg:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
9 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
12 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
15 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
17 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
18 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
20 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
21 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
23 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
26 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
34 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
35 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
37 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
38 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
40 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
41 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
42 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
43 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
44 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
45 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
46 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
47 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
48 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
49 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
50 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
52 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
53 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 37 7.5 8.5 1.5 2.5 NA 0.1 1
55 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
56 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
58 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
59 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
60 #FFBBBB 10 2 1 47 9.5 10.5 1.5 2.5 NA 0.1 1
61 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
62 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
63 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
64 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
65 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
67 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
68 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
70 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
71 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
72 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
73 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 5 3 1 23 4.5 5.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
80 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
81 #FFBBBB 7 3 1 33 6.5 7.5 2.5 3.5 NA 0.1 1
82 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
83 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
84 #FFBBBB 8 3 1 38 7.5 8.5 2.5 3.5 NA 0.1 1
85 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
86 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
87 #FFBBBB 9 3 1 43 8.5 9.5 2.5 3.5 NA 0.1 1
88 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
89 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
90 #FFBBBB 10 3 1 48 9.5 10.5 2.5 3.5 NA 0.1 1
91 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
92 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
93 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
94 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
95 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
96 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1
97 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
98 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
99 #FFBBBB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1
100 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
101 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
102 #FFBBBB 4 4 1 19 3.5 4.5 3.5 4.5 NA 0.1 1
103 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
104 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
105 #FFBBBB 5 4 1 24 4.5 5.5 3.5 4.5 NA 0.1 1
106 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
107 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 29 5.5 6.5 3.5 4.5 NA 0.1 1
109 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
110 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
111 #FFBBBB 7 4 1 34 6.5 7.5 3.5 4.5 NA 0.1 1
112 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
113 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
114 #FFBBBB 8 4 1 39 7.5 8.5 3.5 4.5 NA 0.1 1
115 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
116 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
117 #FFBBBB 9 4 1 44 8.5 9.5 3.5 4.5 NA 0.1 1
118 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
119 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 49 9.5 10.5 3.5 4.5 NA 0.1 1
121 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
122 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
123 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
124 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
125 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 20 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 25 4.5 5.5 4.5 5.5 NA 0.1 1
136 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
137 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 30 5.5 6.5 4.5 5.5 NA 0.1 1
139 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
140 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 35 6.5 7.5 4.5 5.5 NA 0.1 1
142 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
143 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 40 7.5 8.5 4.5 5.5 NA 0.1 1
145 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 9 5 1 45 8.5 9.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 50 9.5 10.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
152 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
153 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
154 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
155 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
156 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1
157 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
158 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
159 #FFBBBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1
160 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
161 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
162 #FFBBBB 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
163 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
164 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
165 #FFBBBB 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
166 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
167 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
168 #FFBBBB 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
169 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
170 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
171 #FFBBBB 7 1 1 31 6.5 7.5 0.5 1.5 NA 0.1 1
172 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
173 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
174 #FFBBBB 8 1 1 36 7.5 8.5 0.5 1.5 NA 0.1 1
175 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
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177 #FFBBBB 9 1 1 41 8.5 9.5 0.5 1.5 NA 0.1 1
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179 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
180 #FFBBBB 10 1 1 46 9.5 10.5 0.5 1.5 NA 0.1 1
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182 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
183 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
184 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
185 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
186 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
187 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
188 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
189 #FFBBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
190 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
191 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
192 #FFBBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
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194 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
195 #FFBBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
196 #FFBBBB 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
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222 #FFBBBB 4 3 1 18 3.5 4.5 2.5 3.5 NA 0.1 1
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228 #FFBBBB 6 3 1 28 5.5 6.5 2.5 3.5 NA 0.1 1
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276 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1
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278 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
279 #FFBBBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1
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23 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
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26 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
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32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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35 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
36 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
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38 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
39 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
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48 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
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50 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
51 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
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53 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
54 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
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56 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
57 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
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60 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
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65 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
66 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
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68 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
69 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
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75 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
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81 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
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102 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
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107 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
108 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
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111 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
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113 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
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117 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
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119 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
120 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
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125 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
126 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
127 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
128 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
129 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
130 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
131 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
132 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
133 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
134 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
135 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
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137 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
138 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
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140 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
141 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
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143 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
144 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
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147 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
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149 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
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155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
158 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
159 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
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165 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
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167 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
168 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
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170 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
171 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
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177 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
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189 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
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216 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
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218 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
219 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
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234 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
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236 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
237 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
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293 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
294 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
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297 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
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299 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
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312 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
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314 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
315 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
316 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
317 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
318 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
319 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
320 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
321 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
322 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
323 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
324 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
325 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
326 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
327 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
328 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
329 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
330 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
331 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
332 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
333 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
334 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
335 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
336 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
337 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
338 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
339 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
340 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
341 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
342 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
343 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
344 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
345 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
346 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
347 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
348 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
349 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
350 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
351 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
352 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
353 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
354 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
355 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
356 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
357 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
358 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
359 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
360 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
361 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
362 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
363 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
364 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
365 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
366 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
367 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
368 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
369 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
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371 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
372 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
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374 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
375 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
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377 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
378 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
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380 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
381 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
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383 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
384 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
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386 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
387 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
388 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
389 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
390 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
391 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
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393 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
394 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
395 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
396 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
397 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
398 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
399 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
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410 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
411 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
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413 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
414 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
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416 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
417 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
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419 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
420 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
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425 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
426 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
427 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
428 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
429 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
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431 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
432 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
433 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
434 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
435 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
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438 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
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440 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
441 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
442 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
443 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
444 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
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446 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
447 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
448 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
449 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
450 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
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454 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
455 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
456 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
457 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
458 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
459 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
460 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
461 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
462 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
463 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
464 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
465 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
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467 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
468 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
469 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
470 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
471 #FFBBBB 7 6 1 66 6.5 7.5 5.5 6.5 NA 0.1 1
472 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
473 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
474 #FFBBBB 8 6 1 76 7.5 8.5 5.5 6.5 NA 0.1 1
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476 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
477 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
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479 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
480 #FFBBBB 10 6 1 96 9.5 10.5 5.5 6.5 NA 0.1 1
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485 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
486 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
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488 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
489 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
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491 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
492 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
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494 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
495 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
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497 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
498 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
499 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
500 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
501 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
502 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
503 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
504 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
505 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
506 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
507 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
508 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
509 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
510 #FFBBBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
511 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
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513 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
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515 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
516 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
517 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
518 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
519 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
520 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
521 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
522 #FFBBBB 4 8 1 38 3.5 4.5 7.5 8.5 NA 0.1 1
523 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
524 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
525 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
526 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
527 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
528 #FFBBBB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
529 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
530 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
531 #FFBBBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
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533 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
534 #FFBBBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
535 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
536 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
537 #FFBBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
538 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
539 #FFBBBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
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545 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
546 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
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548 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
549 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
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551 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
552 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
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554 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
555 #FFBBBB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
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558 #FFBBBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
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560 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
561 #FFBBBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
562 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
563 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
564 #FFBBBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
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566 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
567 #FFBBBB 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
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569 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
570 #FFBBBB 10 9 1 99 9.5 10.5 8.5 9.5 NA 0.1 1
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576 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
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579 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
580 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
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583 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
584 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
585 #FFBBBB 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
586 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
587 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
588 #FFBBBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
589 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
590 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
591 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
592 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
593 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
594 #FFBBBB 8 10 1 80 7.5 8.5 9.5 10.5 NA 0.1 1
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596 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
597 #FFBBBB 9 10 1 90 8.5 9.5 9.5 10.5 NA 0.1 1
598 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
599 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
600 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
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602 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
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605 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
606 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
607 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
608 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
609 #FFBBBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
610 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
611 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
612 #FFBBBB 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
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614 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
615 #FFBBBB 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
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617 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
618 #FFBBBB 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
619 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
620 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
621 #FFBBBB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
622 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
623 #FFBBBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
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626 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
627 #FFBBBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
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629 #FFBBBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
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631 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
632 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
633 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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635 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
636 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
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638 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
639 #FFBBBB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
640 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
641 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
642 #FFBBBB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
643 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
644 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
645 #FFBBBB 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
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647 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
648 #FFBBBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
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650 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
651 #FFBBBB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
652 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
653 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
654 #FFBBBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
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656 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
657 #FFBBBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
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659 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
660 #FFBBBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
661 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
662 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
663 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
664 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
665 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
666 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
667 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
668 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
669 #FFBBBB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
670 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
671 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
672 #FFBBBB 4 3 1 33 3.5 4.5 2.5 3.5 NA 0.1 1
673 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
674 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
675 #FFBBBB 5 3 1 43 4.5 5.5 2.5 3.5 NA 0.1 1
676 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
677 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
678 #FFBBBB 6 3 1 53 5.5 6.5 2.5 3.5 NA 0.1 1
679 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
680 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
681 #FFBBBB 7 3 1 63 6.5 7.5 2.5 3.5 NA 0.1 1
682 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
683 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
684 #FFBBBB 8 3 1 73 7.5 8.5 2.5 3.5 NA 0.1 1
685 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
686 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
687 #FFBBBB 9 3 1 83 8.5 9.5 2.5 3.5 NA 0.1 1
688 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
689 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
690 #FFBBBB 10 3 1 93 9.5 10.5 2.5 3.5 NA 0.1 1
691 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
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693 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
694 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
695 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
696 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
697 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
698 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
699 #FFBBBB 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
700 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
701 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
702 #FFBBBB 4 4 1 34 3.5 4.5 3.5 4.5 NA 0.1 1
703 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
704 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
705 #FFBBBB 5 4 1 44 4.5 5.5 3.5 4.5 NA 0.1 1
706 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
707 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
708 #FFBBBB 6 4 1 54 5.5 6.5 3.5 4.5 NA 0.1 1
709 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
710 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
711 #FFBBBB 7 4 1 64 6.5 7.5 3.5 4.5 NA 0.1 1
712 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
713 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
714 #FFBBBB 8 4 1 74 7.5 8.5 3.5 4.5 NA 0.1 1
715 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
716 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
717 #FFBBBB 9 4 1 84 8.5 9.5 3.5 4.5 NA 0.1 1
718 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
719 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
720 #FFBBBB 10 4 1 94 9.5 10.5 3.5 4.5 NA 0.1 1
721 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
722 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
723 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
724 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
725 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
726 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
727 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
728 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
729 #FFBBBB 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
730 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
731 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
732 #FFBBBB 4 5 1 35 3.5 4.5 4.5 5.5 NA 0.1 1
733 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
734 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
735 #FFBBBB 5 5 1 45 4.5 5.5 4.5 5.5 NA 0.1 1
736 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
737 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
738 #FFBBBB 6 5 1 55 5.5 6.5 4.5 5.5 NA 0.1 1
739 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
740 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
741 #FFBBBB 7 5 1 65 6.5 7.5 4.5 5.5 NA 0.1 1
742 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
743 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
744 #FFBBBB 8 5 1 75 7.5 8.5 4.5 5.5 NA 0.1 1
745 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
746 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
747 #FFBBBB 9 5 1 85 8.5 9.5 4.5 5.5 NA 0.1 1
748 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
749 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
750 #FFBBBB 10 5 1 95 9.5 10.5 4.5 5.5 NA 0.1 1
751 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
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754 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
755 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
756 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
757 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
758 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
759 #FFBBBB 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
760 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
761 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
762 #FFBBBB 4 6 1 36 3.5 4.5 5.5 6.5 NA 0.1 1
763 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
764 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
765 #FFBBBB 5 6 1 46 4.5 5.5 5.5 6.5 NA 0.1 1
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768 #FFBBBB 6 6 1 56 5.5 6.5 5.5 6.5 NA 0.1 1
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777 #FFBBBB 9 6 1 86 8.5 9.5 5.5 6.5 NA 0.1 1
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785 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
786 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
787 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
788 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
789 #FFBBBB 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
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791 #FFBBBB 4 7 1 37 3.5 4.5 6.5 7.5 NA 0.1 1
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795 #FFBBBB 5 7 1 47 4.5 5.5 6.5 7.5 NA 0.1 1
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798 #FFBBBB 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
799 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
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801 #FFBBBB 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
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803 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
804 #FFBBBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
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806 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
807 #FFBBBB 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
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815 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
816 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
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818 #FFBBBB 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
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825 #FFBBBB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
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849 #FFBBBB 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
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852 #FFBBBB 4 9 1 39 3.5 4.5 8.5 9.5 NA 0.1 1
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876 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
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879 #FFBBBB 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
880 #FFBBBB 4 10 1 40 3.5 4.5 9.5 10.5 NA 0.1 1
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890 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
891 #FFBBBB 7 10 1 70 6.5 7.5 9.5 10.5 NA 0.1 1
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898 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
899 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
900 #FFBBBB 10 10 1 100 9.5 10.5 9.5 10.5 NA 0.1 1
alpha width height
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$.all_cats$d2$`vs:am`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
14 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
15 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
16 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
17 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
18 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
19 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
20 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
21 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
22 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
23 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
24 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
25 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
26 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
27 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
28 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
29 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
30 #FFBBBB 2 1 1 3 1.5 2.5 0.5 1.5 NA 0.1 1 NA
31 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
32 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
33 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
34 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
35 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
36 #FFBBBB 2 2 1 4 1.5 2.5 1.5 2.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
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24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
$.all_cats$d2$`vs:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
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202 NA NA NA
203 NA NA NA
204 NA NA NA
205 NA NA NA
206 NA NA NA
207 NA NA NA
208 NA NA NA
209 NA NA NA
210 NA NA NA
211 NA NA NA
212 NA NA NA
213 NA NA NA
214 NA NA NA
215 NA NA NA
216 NA NA NA
217 NA NA NA
218 NA NA NA
219 NA NA NA
220 NA NA NA
221 NA NA NA
222 NA NA NA
223 NA NA NA
224 NA NA NA
225 NA NA NA
226 NA NA NA
227 NA NA NA
228 NA NA NA
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230 NA NA NA
231 NA NA NA
232 NA NA NA
233 NA NA NA
234 NA NA NA
235 NA NA NA
236 NA NA NA
237 NA NA NA
238 NA NA NA
239 NA NA NA
240 NA NA NA
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244 NA NA NA
245 NA NA NA
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247 NA NA NA
248 NA NA NA
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250 NA NA NA
251 NA NA NA
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254 NA NA NA
255 NA NA NA
256 NA NA NA
257 NA NA NA
258 NA NA NA
259 NA NA NA
260 NA NA NA
261 NA NA NA
262 NA NA NA
263 NA NA NA
264 NA NA NA
265 NA NA NA
266 NA NA NA
267 NA NA NA
268 NA NA NA
269 NA NA NA
270 NA NA NA
271 NA NA NA
272 NA NA NA
273 NA NA NA
274 NA NA NA
275 NA NA NA
276 NA NA NA
277 NA NA NA
278 NA NA NA
279 NA NA NA
280 NA NA NA
281 NA NA NA
282 NA NA NA
283 NA NA NA
284 NA NA NA
285 NA NA NA
286 NA NA NA
287 NA NA NA
288 NA NA NA
289 NA NA NA
290 NA NA NA
291 NA NA NA
292 NA NA NA
293 NA NA NA
294 NA NA NA
295 NA NA NA
296 NA NA NA
297 NA NA NA
298 NA NA NA
299 NA NA NA
300 NA NA NA
301 NA NA NA
302 NA NA NA
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305 NA NA NA
306 NA NA NA
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311 NA NA NA
312 NA NA NA
313 NA NA NA
314 NA NA NA
315 NA NA NA
316 NA NA NA
317 NA NA NA
318 NA NA NA
319 NA NA NA
320 NA NA NA
321 NA NA NA
322 NA NA NA
323 NA NA NA
324 NA NA NA
325 NA NA NA
326 NA NA NA
327 NA NA NA
328 NA NA NA
329 NA NA NA
330 NA NA NA
331 NA NA NA
332 NA NA NA
333 NA NA NA
334 NA NA NA
335 NA NA NA
336 NA NA NA
337 NA NA NA
338 NA NA NA
339 NA NA NA
340 NA NA NA
341 NA NA NA
342 NA NA NA
343 NA NA NA
344 NA NA NA
345 NA NA NA
346 NA NA NA
347 NA NA NA
348 NA NA NA
349 NA NA NA
350 NA NA NA
351 NA NA NA
352 NA NA NA
353 NA NA NA
354 NA NA NA
355 NA NA NA
356 NA NA NA
357 NA NA NA
358 NA NA NA
359 NA NA NA
360 NA NA NA
361 NA NA NA
362 NA NA NA
363 NA NA NA
364 NA NA NA
365 NA NA NA
366 NA NA NA
367 NA NA NA
368 NA NA NA
369 NA NA NA
370 NA NA NA
371 NA NA NA
372 NA NA NA
373 NA NA NA
374 NA NA NA
375 NA NA NA
376 NA NA NA
377 NA NA NA
378 NA NA NA
379 NA NA NA
380 NA NA NA
381 NA NA NA
382 NA NA NA
383 NA NA NA
384 NA NA NA
385 NA NA NA
386 NA NA NA
387 NA NA NA
388 NA NA NA
389 NA NA NA
390 NA NA NA
391 NA NA NA
392 NA NA NA
393 NA NA NA
394 NA NA NA
395 NA NA NA
396 NA NA NA
397 NA NA NA
398 NA NA NA
399 NA NA NA
400 NA NA NA
401 NA NA NA
402 NA NA NA
403 NA NA NA
404 NA NA NA
405 NA NA NA
406 NA NA NA
407 NA NA NA
408 NA NA NA
409 NA NA NA
410 NA NA NA
411 NA NA NA
412 NA NA NA
413 NA NA NA
414 NA NA NA
415 NA NA NA
416 NA NA NA
417 NA NA NA
418 NA NA NA
419 NA NA NA
420 NA NA NA
421 NA NA NA
422 NA NA NA
423 NA NA NA
424 NA NA NA
425 NA NA NA
426 NA NA NA
427 NA NA NA
428 NA NA NA
429 NA NA NA
430 NA NA NA
431 NA NA NA
432 NA NA NA
433 NA NA NA
434 NA NA NA
435 NA NA NA
436 NA NA NA
437 NA NA NA
438 NA NA NA
439 NA NA NA
440 NA NA NA
441 NA NA NA
442 NA NA NA
443 NA NA NA
444 NA NA NA
445 NA NA NA
446 NA NA NA
447 NA NA NA
448 NA NA NA
449 NA NA NA
450 NA NA NA
451 NA NA NA
452 NA NA NA
453 NA NA NA
454 NA NA NA
455 NA NA NA
456 NA NA NA
457 NA NA NA
458 NA NA NA
459 NA NA NA
460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
481 NA NA NA
482 NA NA NA
483 NA NA NA
484 NA NA NA
485 NA NA NA
486 NA NA NA
487 NA NA NA
488 NA NA NA
489 NA NA NA
490 NA NA NA
491 NA NA NA
492 NA NA NA
493 NA NA NA
494 NA NA NA
495 NA NA NA
496 NA NA NA
497 NA NA NA
498 NA NA NA
499 NA NA NA
500 NA NA NA
501 NA NA NA
502 NA NA NA
503 NA NA NA
504 NA NA NA
505 NA NA NA
506 NA NA NA
507 NA NA NA
508 NA NA NA
509 NA NA NA
510 NA NA NA
511 NA NA NA
512 NA NA NA
513 NA NA NA
514 NA NA NA
515 NA NA NA
516 NA NA NA
517 NA NA NA
518 NA NA NA
519 NA NA NA
520 NA NA NA
521 NA NA NA
522 NA NA NA
523 NA NA NA
524 NA NA NA
525 NA NA NA
526 NA NA NA
527 NA NA NA
528 NA NA NA
529 NA NA NA
530 NA NA NA
531 NA NA NA
532 NA NA NA
533 NA NA NA
534 NA NA NA
535 NA NA NA
536 NA NA NA
537 NA NA NA
538 NA NA NA
539 NA NA NA
540 NA NA NA
541 NA NA NA
542 NA NA NA
543 NA NA NA
544 NA NA NA
545 NA NA NA
546 NA NA NA
547 NA NA NA
548 NA NA NA
549 NA NA NA
550 NA NA NA
551 NA NA NA
552 NA NA NA
553 NA NA NA
554 NA NA NA
555 NA NA NA
556 NA NA NA
557 NA NA NA
558 NA NA NA
559 NA NA NA
560 NA NA NA
561 NA NA NA
562 NA NA NA
563 NA NA NA
564 NA NA NA
565 NA NA NA
566 NA NA NA
567 NA NA NA
568 NA NA NA
569 NA NA NA
570 NA NA NA
571 NA NA NA
572 NA NA NA
573 NA NA NA
574 NA NA NA
575 NA NA NA
576 NA NA NA
$.all_cats$d2$`vs:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
20 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
21 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
22 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
23 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
24 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
25 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
26 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
27 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
28 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
29 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
30 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
31 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
32 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
33 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
34 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
35 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
36 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
37 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
38 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
39 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
40 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
41 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
42 #FFBBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
43 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
44 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
45 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
46 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
47 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
48 #FFBBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
49 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
50 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
51 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
52 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
53 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
54 #FFBBBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
$.all_cats$d2$`vs:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
32 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
33 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
34 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
35 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
36 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
37 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
38 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
39 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
40 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
41 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
42 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
43 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
44 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
45 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
46 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
47 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
48 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
49 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
50 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
51 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
52 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
53 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
54 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
55 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
56 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
57 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
58 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
59 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
60 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
61 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
62 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
63 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
64 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
65 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
66 #FFBBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
67 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
68 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
69 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
70 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
71 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
72 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
73 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
74 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
75 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
76 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
77 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
78 #FFBBBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
79 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
80 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
81 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
82 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
83 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
84 #FFBBBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
85 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
86 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
87 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
88 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
89 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
90 #FFBBBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$.all_cats$d2$`vs:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
11 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
12 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
17 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
18 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
23 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
24 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
29 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
30 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
35 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
36 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
41 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
42 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
47 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
48 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
53 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
54 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
59 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
60 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
62 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
63 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
64 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
65 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
66 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
67 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
68 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
69 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
70 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
71 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
72 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
73 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
74 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
75 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
76 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
77 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
78 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
79 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
80 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
81 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
82 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
83 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
84 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
85 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
86 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
87 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
88 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
89 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
90 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
91 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
92 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
93 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
94 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
95 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
96 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
97 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
98 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
99 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
100 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
101 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
102 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
103 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
104 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
105 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
106 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
107 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
108 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
109 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
110 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
111 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
112 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
113 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
114 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
115 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
116 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
117 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
118 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
119 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
120 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
121 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
122 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
123 #FFBBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
124 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
125 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
126 #FFBBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
127 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
128 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
129 #FFBBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
130 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
131 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
132 #FFBBBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
133 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
134 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
135 #FFBBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
136 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
137 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
138 #FFBBBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
139 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
140 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
141 #FFBBBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
142 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
143 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
144 #FFBBBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
145 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
146 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
147 #FFBBBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
148 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
149 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
150 #FFBBBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
151 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #FFBBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #FFBBBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
158 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
159 #FFBBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
160 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
161 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
162 #FFBBBB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
163 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
164 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
165 #FFBBBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
166 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
167 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
168 #FFBBBB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
169 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
170 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
171 #FFBBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
172 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
173 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
174 #FFBBBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
175 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
176 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
177 #FFBBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
178 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
179 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
180 #FFBBBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
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11 NA NA NA
12 NA NA NA
13 NA NA NA
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15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
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41 NA NA NA
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55 NA NA NA
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61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
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67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
$.all_cats$d2$`am:model`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFBDBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBBDFF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBC9FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #BBC5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFBDBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBC9FF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
12 #BBBDFF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #BBC5FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFBDBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBCBFF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
18 #BBC3FF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFC6BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBCBFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #BBC5FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #FFBDBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBCBFF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
24 #BBC3FF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
25 #FFC6BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #BBC5FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #FFBDBB 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
29 #BBCBFF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
30 #BBC3FF 2 5 1 37 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFC5BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBC4FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #FFBDBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
35 #BBCBFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
36 #BBC3FF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFC5BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBC4FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #FFBDBB 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
41 #BBCBFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
42 #BBC3FF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
43 #FFC5BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC4FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #FFAFAA 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
47 #BBCBFF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
48 #BBC0FF 2 8 1 40 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFC8BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC4FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #FFAFAA 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
53 #BBCBFF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
54 #BBC0FF 2 9 1 41 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC4FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #FFB9B8 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
59 #BBCBFF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
60 #BBC2FF 2 10 1 42 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC7BB 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
62 #BBCBFF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
63 #BBC4FF 1 11 1 11 0.5 1.5 10.5 11.5 NA 0.1 1
64 #FFB9B8 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
65 #BBCBFF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
66 #BBC2FF 2 11 1 43 1.5 2.5 10.5 11.5 NA 0.1 1
67 #FFC5BB 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
68 #BBCBFF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
69 #BBC5FF 1 12 1 12 0.5 1.5 11.5 12.5 NA 0.1 1
70 #FFBDBB 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
71 #BBCBFF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
72 #BBC4FF 2 12 1 44 1.5 2.5 11.5 12.5 NA 0.1 1
73 #FFBFBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
74 #FFCBBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
75 #BBC5FF 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
76 #FFBDBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
77 #FFCBBB 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
78 #BBC4FF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
79 #FFBFBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
80 #FFCBBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
81 #BBC5FF 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
82 #FFBDBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
83 #FFCBBB 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
84 #BBC4FF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
85 #FFBFBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
86 #FFCBBB 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
87 #BBC5FF 1 15 1 15 0.5 1.5 14.5 15.5 NA 0.1 1
88 #FFBDBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
89 #FFCBBB 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
90 #BBC4FF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
91 #FFBFBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
92 #FFCBBB 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
93 #BBC5FF 1 16 1 16 0.5 1.5 15.5 16.5 NA 0.1 1
94 #FFBDBB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
95 #FFCABB 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
96 #BBC4FF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
97 #FFBFBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
98 #FFCBBB 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
99 #BBC4FF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
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387 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
388 #FFBBBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
389 #FFCCBB 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
390 #BBBFFF 2 1 1 33 1.5 2.5 0.5 1.5 NA 0.1 1
391 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
392 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
393 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
394 #FFBBBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
395 #FFCCBB 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
396 #BBBFFF 2 2 1 34 1.5 2.5 1.5 2.5 NA 0.1 1
397 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
398 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
399 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
400 #FFBBBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
401 #FFCCBB 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
402 #BBBFFF 2 3 1 35 1.5 2.5 2.5 3.5 NA 0.1 1
403 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
404 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
405 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
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407 #FFCCBB 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
408 #BBBFFF 2 4 1 36 1.5 2.5 3.5 4.5 NA 0.1 1
409 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
410 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
411 #FFCCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
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419 #FFCCBB 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
420 #BBBFFF 2 6 1 38 1.5 2.5 5.5 6.5 NA 0.1 1
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422 #FFCCBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
423 #BBCCFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
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426 #BBBFFF 2 7 1 39 1.5 2.5 6.5 7.5 NA 0.1 1
427 #FFC6BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
428 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
429 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
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457 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
458 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
459 #FFCCBB 1 13 1 13 0.5 1.5 12.5 13.5 NA 0.1 1
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462 #BBBFFF 2 13 1 45 1.5 2.5 12.5 13.5 NA 0.1 1
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464 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
465 #FFCCBB 1 14 1 14 0.5 1.5 13.5 14.5 NA 0.1 1
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468 #BBBFFF 2 14 1 46 1.5 2.5 13.5 14.5 NA 0.1 1
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474 #BBBFFF 2 15 1 47 1.5 2.5 14.5 15.5 NA 0.1 1
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480 #BBBFFF 2 16 1 48 1.5 2.5 15.5 16.5 NA 0.1 1
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483 #BBCCFF 1 17 1 17 0.5 1.5 16.5 17.5 NA 0.1 1
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489 #BBCCFF 1 18 1 18 0.5 1.5 17.5 18.5 NA 0.1 1
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491 #FFCCBB 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
492 #BBBFFF 2 18 1 50 1.5 2.5 17.5 18.5 NA 0.1 1
493 #FFC6BB 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
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495 #BBC8FF 1 19 1 19 0.5 1.5 18.5 19.5 NA 0.1 1
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531 #BBC5FF 1 25 1 25 0.5 1.5 24.5 25.5 NA 0.1 1
532 #FFBABA 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
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534 #B1A8FF 2 25 1 57 1.5 2.5 24.5 25.5 NA 0.1 1
535 #BBCCFF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
536 #BBC7FF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
537 #BBC5FF 1 26 1 26 0.5 1.5 25.5 26.5 NA 0.1 1
538 #FFBABA 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
539 #BBC8FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
540 #B1A8FF 2 26 1 58 1.5 2.5 25.5 26.5 NA 0.1 1
541 #BBCCFF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
542 #BBC7FF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
543 #BBC4FF 1 27 1 27 0.5 1.5 26.5 27.5 NA 0.1 1
544 #FFBABA 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
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546 #B2AAFF 2 27 1 59 1.5 2.5 26.5 27.5 NA 0.1 1
547 #BBCCFF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
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549 #BBC4FF 1 28 1 28 0.5 1.5 27.5 28.5 NA 0.1 1
550 #FFBAB9 2 28 1 60 1.5 2.5 27.5 28.5 NA 0.1 1
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553 #FFCCBB 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
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555 #BBC4FF 1 29 1 29 0.5 1.5 28.5 29.5 NA 0.1 1
556 #FFA69D 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
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558 #A494FF 2 29 1 61 1.5 2.5 28.5 29.5 NA 0.1 1
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570 #AB9EFF 2 31 1 63 1.5 2.5 30.5 31.5 NA 0.1 1
571 #FFCABB 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
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573 #BBC3FF 1 32 1 32 0.5 1.5 31.5 32.5 NA 0.1 1
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460 NA NA NA
461 NA NA NA
462 NA NA NA
463 NA NA NA
464 NA NA NA
465 NA NA NA
466 NA NA NA
467 NA NA NA
468 NA NA NA
469 NA NA NA
470 NA NA NA
471 NA NA NA
472 NA NA NA
473 NA NA NA
474 NA NA NA
475 NA NA NA
476 NA NA NA
477 NA NA NA
478 NA NA NA
479 NA NA NA
480 NA NA NA
481 NA NA NA
482 NA NA NA
483 NA NA NA
484 NA NA NA
485 NA NA NA
486 NA NA NA
487 NA NA NA
488 NA NA NA
489 NA NA NA
490 NA NA NA
491 NA NA NA
492 NA NA NA
493 NA NA NA
494 NA NA NA
495 NA NA NA
496 NA NA NA
497 NA NA NA
498 NA NA NA
499 NA NA NA
500 NA NA NA
501 NA NA NA
502 NA NA NA
503 NA NA NA
504 NA NA NA
505 NA NA NA
506 NA NA NA
507 NA NA NA
508 NA NA NA
509 NA NA NA
510 NA NA NA
511 NA NA NA
512 NA NA NA
513 NA NA NA
514 NA NA NA
515 NA NA NA
516 NA NA NA
517 NA NA NA
518 NA NA NA
519 NA NA NA
520 NA NA NA
521 NA NA NA
522 NA NA NA
523 NA NA NA
524 NA NA NA
525 NA NA NA
526 NA NA NA
527 NA NA NA
528 NA NA NA
529 NA NA NA
530 NA NA NA
531 NA NA NA
532 NA NA NA
533 NA NA NA
534 NA NA NA
535 NA NA NA
536 NA NA NA
537 NA NA NA
538 NA NA NA
539 NA NA NA
540 NA NA NA
541 NA NA NA
542 NA NA NA
543 NA NA NA
544 NA NA NA
545 NA NA NA
546 NA NA NA
547 NA NA NA
548 NA NA NA
549 NA NA NA
550 NA NA NA
551 NA NA NA
552 NA NA NA
553 NA NA NA
554 NA NA NA
555 NA NA NA
556 NA NA NA
557 NA NA NA
558 NA NA NA
559 NA NA NA
560 NA NA NA
561 NA NA NA
562 NA NA NA
563 NA NA NA
564 NA NA NA
565 NA NA NA
566 NA NA NA
567 NA NA NA
568 NA NA NA
569 NA NA NA
570 NA NA NA
571 NA NA NA
572 NA NA NA
573 NA NA NA
574 NA NA NA
575 NA NA NA
576 NA NA NA
$.all_cats$d2$`am:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FF7F6A 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #FFC7BB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBBFFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFB8B6 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #FFC2BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #FFC8BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FF432A 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFA399 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBCBFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #FFB8B6 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #FFC2BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FF0000 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FF573D 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #FF8977 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FF9A8D 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
20 #FFCABB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
21 #BBC1FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
22 #FFB4B1 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
23 #FFCABB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
24 #BBC0FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
25 #FF836F 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
26 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
27 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
28 #FF9E92 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
29 #FFC1BB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
30 #BBC5FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
31 #FF836F 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
32 #FFC6BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
33 #BBC1FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
34 #FFB3B0 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
35 #BBCBFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
36 #B5B0FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
37 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
38 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
39 #BBC2FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
40 #FF7E69 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
41 #BBC8FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
42 #9F8CFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1 NA
43 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
44 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
45 #B9B7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
46 #FFBEBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
47 #B4ADFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
48 #5134FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1 NA
49 #BBC7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
50 #BBC2FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
51 #B9B7FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
52 #FFBEBB 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
53 #AB9FFF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
54 #0000FF 2 3 1 6 1.5 2.5 2.5 3.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
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10 NA NA
11 NA NA
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14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
$.all_cats$d2$`am:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFC4BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC5FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
9 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
10 #FFBBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
11 #FFCCBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
12 #BBC2FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
13 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
15 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
16 #FFC2BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
17 #FFCCBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
18 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
19 #FFC8BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
20 #BBC2FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
21 #B2ABFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
22 #B8B5FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
23 #AB9EFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
24 #9781FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
25 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
26 #BBC2FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
27 #BBBCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
28 #BAB9FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
29 #AB9EFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
30 #8A6FFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
31 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
32 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
33 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
34 #FFCABB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
35 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
36 #FFCBBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
37 #FFB8B7 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
38 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
39 #BBC2FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
40 #FFCCBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
41 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
42 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
43 #FFC1BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
44 #FFCBBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
45 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
46 #FFC6BB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
47 #BBCCFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
48 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
49 #FFB4B1 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
50 #FFBCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
51 #FFC2BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
52 #FF452C 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
53 #FF6A51 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
54 #FF8B79 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
55 #FFB4B2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
56 #FFBCBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
57 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
58 #FF0000 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
59 #FF6A51 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
60 #FF8D7B 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
61 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
62 #EDD9C5 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
63 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
64 #FFC2BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
65 #EDD9C5 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
66 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
67 #FFC6BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
68 #F3DAC2 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
69 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
70 #FFC2BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
71 #FFDDBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
72 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
73 #FFCCBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
74 #F3DAC2 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
75 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
76 #FFBCBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
77 #FFDDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
78 #BBC2FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
79 #FCDCBC 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
80 #F3DAC2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
81 #EDD9C4 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
82 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
83 #FFDDBB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
84 #BBC6FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
85 #FFC6BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
86 #F3DAC2 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
87 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
88 #FFBCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
89 #FFDDBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
90 #BBC2FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
$.all_cats$d2$`am:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
8 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
9 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
10 #FFC4BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
11 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
12 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
13 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
14 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
15 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
16 #FFC4BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
17 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
18 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
19 #FFCABB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
20 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
21 #BBC9FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
22 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
23 #BBC9FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
24 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
25 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
26 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
27 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
28 #BBC9FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
29 #BBC7FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
30 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
31 #FFCBBB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
32 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
33 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
34 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
35 #BBC7FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
36 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
37 #FFCBBB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
38 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
39 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
40 #BBC8FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
41 #BBC6FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
42 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
43 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
44 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
45 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
46 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
47 #BBC6FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
48 #BBC5FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
49 #FFCBBB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
50 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
51 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
52 #BBC8FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
53 #BBC6FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
54 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
55 #FFCBBB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
56 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
57 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
58 #BBC8FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
59 #BBC6FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
60 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
61 #FFC1BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
62 #FFC7BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
63 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
64 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
65 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
66 #FFC7BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
67 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
68 #FFC7BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
69 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
70 #FFBFBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
71 #FFC7BB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
72 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
73 #FFB3B0 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
74 #FFC7BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
75 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
76 #FFBFBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
77 #FFC7BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
78 #BBCCFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
79 #FFB1AD 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
80 #FFC0BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
81 #FFCCBB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
82 #FFA196 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
83 #FFAEA8 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
84 #FFBDBB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
85 #FF8A78 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
86 #FFB6B4 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
87 #FFC0BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
88 #FF5F45 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
89 #FF674E 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
90 #FF6C53 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
91 #FF8976 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
92 #FFB6B4 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
93 #FFC7BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
94 #FF3A22 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
95 #FF573D 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
96 #FF6950 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
97 #FF8976 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
98 #FFB6B4 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
99 #FFC7BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
100 #FF301A 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
101 #FF4F35 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
102 #FF684F 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
103 #FF8A78 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
104 #FFB6B4 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
105 #FFC0BB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
106 #FF331D 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
107 #FF4F35 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
108 #FF684F 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
109 #FF8976 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
110 #FFB6B4 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
111 #FFC7BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
112 #FF0000 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
113 #FF4F35 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
114 #FF6950 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
115 #FF8976 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
116 #FFB6B4 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
117 #FFC7BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
118 #FF0000 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
119 #FF4F35 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
120 #FF6950 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
121 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
122 #C8D7E8 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
123 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
124 #FFC6BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
125 #C8D7E8 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
126 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
127 #FFC4BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
128 #C8D7E8 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
129 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
130 #FFBEBB 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
131 #E9D7C7 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
132 #BBCAFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
133 #FFC4BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
134 #C8D7E8 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
135 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
136 #FFBEBB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
137 #E9D7C7 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
138 #BBCAFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
139 #C8D7E8 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
140 #C8D7E9 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
141 #C8D7E9 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
142 #FFC6BB 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
143 #C8D7E9 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
144 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
145 #FFC8BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
146 #C8D7E9 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
147 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
148 #FFBEBB 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
149 #C8D7E9 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
150 #BBCAFF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
151 #FFC4BB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
152 #C8D7E9 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
153 #BBCBFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
154 #FFBEBB 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
155 #C8D7E9 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
156 #BBCAFF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
157 #FFC4BB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
158 #C8D7E9 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
159 #BBCBFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
160 #FFC6BB 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
161 #C8D7E9 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
162 #BBCBFF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
163 #FFCCBB 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
164 #C8D7E9 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
165 #BBCCFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
166 #FFC2BB 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
167 #C8D7E9 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
168 #BBCBFF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
169 #FFC4BB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
170 #C8D7E9 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
171 #BBCBFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
172 #FFBEBB 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
173 #C8D7E9 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
174 #BBCAFF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
175 #FFC4BB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
176 #C8D7E9 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
177 #BBCBFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
178 #FFBEBB 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
179 #C8D7E9 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
180 #BBCAFF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
alpha width height
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
7 NA NA NA
8 NA NA NA
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA
21 NA NA NA
22 NA NA NA
23 NA NA NA
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
29 NA NA NA
30 NA NA NA
31 NA NA NA
32 NA NA NA
33 NA NA NA
34 NA NA NA
35 NA NA NA
36 NA NA NA
37 NA NA NA
38 NA NA NA
39 NA NA NA
40 NA NA NA
41 NA NA NA
42 NA NA NA
43 NA NA NA
44 NA NA NA
45 NA NA NA
46 NA NA NA
47 NA NA NA
48 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
53 NA NA NA
54 NA NA NA
55 NA NA NA
56 NA NA NA
57 NA NA NA
58 NA NA NA
59 NA NA NA
60 NA NA NA
61 NA NA NA
62 NA NA NA
63 NA NA NA
64 NA NA NA
65 NA NA NA
66 NA NA NA
67 NA NA NA
68 NA NA NA
69 NA NA NA
70 NA NA NA
71 NA NA NA
72 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
80 NA NA NA
81 NA NA NA
82 NA NA NA
83 NA NA NA
84 NA NA NA
85 NA NA NA
86 NA NA NA
87 NA NA NA
88 NA NA NA
89 NA NA NA
90 NA NA NA
91 NA NA NA
92 NA NA NA
93 NA NA NA
94 NA NA NA
95 NA NA NA
96 NA NA NA
97 NA NA NA
98 NA NA NA
99 NA NA NA
100 NA NA NA
101 NA NA NA
102 NA NA NA
103 NA NA NA
104 NA NA NA
105 NA NA NA
106 NA NA NA
107 NA NA NA
108 NA NA NA
109 NA NA NA
110 NA NA NA
111 NA NA NA
112 NA NA NA
113 NA NA NA
114 NA NA NA
115 NA NA NA
116 NA NA NA
117 NA NA NA
118 NA NA NA
119 NA NA NA
120 NA NA NA
121 NA NA NA
122 NA NA NA
123 NA NA NA
124 NA NA NA
125 NA NA NA
126 NA NA NA
127 NA NA NA
128 NA NA NA
129 NA NA NA
130 NA NA NA
131 NA NA NA
132 NA NA NA
133 NA NA NA
134 NA NA NA
135 NA NA NA
136 NA NA NA
137 NA NA NA
138 NA NA NA
139 NA NA NA
140 NA NA NA
141 NA NA NA
142 NA NA NA
143 NA NA NA
144 NA NA NA
145 NA NA NA
146 NA NA NA
147 NA NA NA
148 NA NA NA
149 NA NA NA
150 NA NA NA
151 NA NA NA
152 NA NA NA
153 NA NA NA
154 NA NA NA
155 NA NA NA
156 NA NA NA
157 NA NA NA
158 NA NA NA
159 NA NA NA
160 NA NA NA
161 NA NA NA
162 NA NA NA
163 NA NA NA
164 NA NA NA
165 NA NA NA
166 NA NA NA
167 NA NA NA
168 NA NA NA
169 NA NA NA
170 NA NA NA
171 NA NA NA
172 NA NA NA
173 NA NA NA
174 NA NA NA
175 NA NA NA
176 NA NA NA
177 NA NA NA
178 NA NA NA
179 NA NA NA
180 NA NA NA
$.all_cats$d2$`model:gear`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #FFCBBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #FFCBBB 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCCFF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC9FF 2 1 1 4 1.5 2.5 0.5 1.5 NA 0.1 1
7 #FFC5BB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
8 #FFCABB 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC9FF 3 1 1 7 2.5 3.5 0.5 1.5 NA 0.1 1
10 #FFC8BB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
11 #FFCBBB 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC9FF 4 1 1 10 3.5 4.5 0.5 1.5 NA 0.1 1
13 #FFCBBB 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBCAFF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC6FF 5 1 1 13 4.5 5.5 0.5 1.5 NA 0.1 1
16 #FFC8BB 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBCBFF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC6FF 6 1 1 16 5.5 6.5 0.5 1.5 NA 0.1 1
19 #FFC6BB 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBCBFF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC4FF 7 1 1 19 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFC8BB 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBCBFF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC5FF 8 1 1 22 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFC8BB 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBCBFF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC5FF 9 1 1 25 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFC2BB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
29 #FFCCBB 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC4FF 10 1 1 28 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFC2BB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
32 #FFCCBB 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC4FF 11 1 1 31 10.5 11.5 0.5 1.5 NA 0.1 1
34 #FFC6BB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
35 #FFCCBB 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC8FF 12 1 1 34 11.5 12.5 0.5 1.5 NA 0.1 1
37 #FFC3BB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
38 #FFCABB 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC8FF 13 1 1 37 12.5 13.5 0.5 1.5 NA 0.1 1
40 #FFC3BB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
41 #FFCABB 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC8FF 14 1 1 40 13.5 14.5 0.5 1.5 NA 0.1 1
43 #FFC3BB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
44 #FFCABB 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC8FF 15 1 1 43 14.5 15.5 0.5 1.5 NA 0.1 1
46 #FFC3BB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
47 #FFCABB 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC8FF 16 1 1 46 15.5 16.5 0.5 1.5 NA 0.1 1
49 #FFBDBB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
50 #FFCABB 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC6FF 17 1 1 49 16.5 17.5 0.5 1.5 NA 0.1 1
52 #FFBFBB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
53 #FFCABB 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC7FF 18 1 1 52 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFC2BB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
56 #FFCABB 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC8FF 19 1 1 55 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFBDBB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
59 #FFCABB 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC6FF 20 1 1 58 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFBCBB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
62 #FFCABB 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC6FF 21 1 1 61 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFBFBB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
65 #FFCABB 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC6FF 22 1 1 64 21.5 22.5 0.5 1.5 NA 0.1 1
67 #FFBFBB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
68 #FFCABB 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC7FF 23 1 1 67 22.5 23.5 0.5 1.5 NA 0.1 1
70 #FFBFBB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
71 #FFCABB 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC7FF 24 1 1 70 23.5 24.5 0.5 1.5 NA 0.1 1
73 #FFBFBB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
74 #FFCABB 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC7FF 25 1 1 73 24.5 25.5 0.5 1.5 NA 0.1 1
76 #FFBFBB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
77 #FFCABB 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC7FF 26 1 1 76 25.5 26.5 0.5 1.5 NA 0.1 1
79 #FFBFBB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
80 #FFCABB 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC7FF 27 1 1 79 26.5 27.5 0.5 1.5 NA 0.1 1
82 #FFB5B2 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
83 #FFCABB 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC4FF 28 1 1 82 27.5 28.5 0.5 1.5 NA 0.1 1
85 #FFBBBB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
86 #FFCABB 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC4FF 29 1 1 85 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFBBBB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
89 #FFCABB 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC4FF 30 1 1 88 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFB0AB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
92 #FFCABB 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC3FF 31 1 1 91 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFB0AB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
95 #FFCABB 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC3FF 32 1 1 94 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #FFCCBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #FFCBBB 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBCCFF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC9FF 2 2 1 5 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFC5BB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
104 #FFCABB 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC9FF 3 2 1 8 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFC8BB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
107 #FFCBBB 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC9FF 4 2 1 11 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFCBBB 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBCAFF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC6FF 5 2 1 14 4.5 5.5 1.5 2.5 NA 0.1 1
112 #FFC8BB 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBCBFF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 17 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC6BB 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBCBFF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC4FF 7 2 1 20 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFC8BB 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBCBFF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC5FF 8 2 1 23 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFC8BB 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBCBFF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC5FF 9 2 1 26 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFC2BB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
125 #FFCCBB 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC4FF 10 2 1 29 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFC2BB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
128 #FFCCBB 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC4FF 11 2 1 32 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFC6BB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
131 #FFCCBB 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC8FF 12 2 1 35 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFC3BB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
134 #FFCABB 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC8FF 13 2 1 38 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFC3BB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
137 #FFCABB 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC8FF 14 2 1 41 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFC2BB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
140 #FFCABB 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC8FF 15 2 1 44 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFC2BB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
143 #FFCABB 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC8FF 16 2 1 47 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFBDBB 17 2 1 50 16.5 17.5 1.5 2.5 NA 0.1 1
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10 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
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12 #BBC4FF 4 1 1 16 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC4FF 5 1 1 21 4.5 5.5 0.5 1.5 NA 0.1 1
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16 #BBC5FF 6 1 1 26 5.5 6.5 0.5 1.5 NA 0.1 1
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96 #8467FF 32 1 1 156 31.5 32.5 0.5 1.5 NA 0.1 1
97 #FFCBBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBC4FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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100 #FFCBBB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
102 #ADA2FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1
103 #FFCBBB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBC4FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
105 #ADA2FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1
106 #FFCBBB 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBC4FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
108 #ADA2FF 4 2 1 17 3.5 4.5 1.5 2.5 NA 0.1 1
109 #FFCBBB 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC4FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
111 #ADA1FF 5 2 1 22 4.5 5.5 1.5 2.5 NA 0.1 1
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114 #A899FF 6 2 1 27 5.5 6.5 1.5 2.5 NA 0.1 1
115 #FFC6BB 7 2 1 32 6.5 7.5 1.5 2.5 NA 0.1 1
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123 #6849FF 9 2 1 42 8.5 9.5 1.5 2.5 NA 0.1 1
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192 #937BFF 32 2 1 157 31.5 32.5 1.5 2.5 NA 0.1 1
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198 #BBBEFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1
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200 #BBC3FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1
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$.all_cats$d2$`model:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBC7FF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBC7FF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
11 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
12 #BBC7FF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
13 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
14 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
15 #BBC7FF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
16 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
17 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
18 #BBC7FF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
19 #BBC8FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
20 #BBC7FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
21 #BBC5FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
22 #FFCCBB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
23 #BBC7FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
24 #BBC1FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
25 #FFCCBB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
26 #BBC7FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
27 #BBC1FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
28 #FFCCBB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
29 #BBC7FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
30 #BBC1FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
31 #FFCCBB 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
32 #BBC7FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
33 #BBC1FF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
34 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
35 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
36 #BBC7FF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
37 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
38 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
39 #BBC7FF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
40 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
41 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
42 #BBC7FF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
43 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
44 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
45 #BBC7FF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
46 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
47 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
48 #BBC7FF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
49 #BBC8FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
50 #BBC7FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
51 #BBC5FF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
52 #BBC9FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
53 #BBC7FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
54 #BBC4FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
55 #FFCCBB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
56 #BBC7FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
57 #BBC1FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
58 #FFCCBB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
59 #BBC7FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
60 #BBC1FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
61 #FFCCBB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
62 #BBC7FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
63 #BBC1FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
64 #FFCCBB 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
65 #BBC7FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
66 #BBC1FF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
67 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
68 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
69 #BBC7FF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
70 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
71 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
72 #BBC7FF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
73 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
74 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
75 #BBC7FF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
76 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
77 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
78 #BBC7FF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
79 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
80 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
81 #BBC7FF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
82 #BBC8FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
83 #BBC7FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
84 #BBC5FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
85 #BBCBFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
86 #BBC7FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
87 #BBC2FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
88 #FFCCBB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
89 #BBC7FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
90 #BBC1FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
91 #FFCCBB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
92 #BBC7FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
93 #BBC0FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
94 #FFCCBB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
95 #BBC7FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
96 #BBC0FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
97 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
98 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
99 #BBC7FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
100 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
101 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
102 #BBC7FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
103 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
104 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
105 #BBC7FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
106 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
107 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
108 #BBC7FF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
109 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
110 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
111 #BBC7FF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
112 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
113 #BBC7FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
114 #BBC6FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
115 #BBC8FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
116 #BBC7FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
117 #BBC5FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
118 #FFCCBB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
119 #BBC7FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
120 #BBC0FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
121 #FFCCBB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
122 #BBC7FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
123 #BBC0FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
124 #FFCCBB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
125 #BBC7FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
126 #BBC0FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
127 #FFCCBB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
128 #BBC7FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
129 #BBC0FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
130 #FFCCBB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
131 #BBC7FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
132 #BBC0FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
133 #FFCCBB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
134 #BBC7FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
135 #BBC0FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
136 #FFCCBB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
137 #BBC7FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
138 #BBC0FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
139 #FFCCBB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
140 #BBC7FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
141 #BBC0FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
142 #FFCCBB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
143 #BBC7FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
144 #BBC0FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
145 #FFCBBB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
146 #BBC7FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
147 #BBBEFF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
148 #FFCABB 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
149 #BBC7FF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
150 #BBBDFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
151 #FFC8BB 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
152 #BBC7FF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
153 #B8B6FF 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
154 #FFC8BB 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
155 #BBC7FF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
156 #B8B5FF 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
157 #FFC8BB 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
158 #BBC7FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
159 #B7B4FF 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
160 #FFC8BB 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
161 #BBC7FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
162 #B7B4FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
163 #FFCBBB 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
164 #BBC7FF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
165 #BBBFFF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
166 #FFCBBB 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
167 #BBC7FF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
168 #BBBFFF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
169 #FFCBBB 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
170 #BBC7FF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
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1501 #FF8A77 21 6 1 206 20.5 21.5 5.5 6.5 NA 0.1 1
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1504 #FF8B79 22 6 1 216 21.5 22.5 5.5 6.5 NA 0.1 1
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1554 #BBC9FF 6 7 1 57 5.5 6.5 6.5 7.5 NA 0.1 1
1555 #FF8976 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
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1557 #BBC8FF 7 7 1 67 6.5 7.5 6.5 7.5 NA 0.1 1
1558 #FF8673 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
1559 #FFBFBB 8 7 1 77 7.5 8.5 6.5 7.5 NA 0.1 1
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1561 #FF8672 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
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1563 #BBC6FF 9 7 1 87 8.5 9.5 6.5 7.5 NA 0.1 1
1564 #FF8672 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
1565 #FFBFBB 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
1566 #BBC8FF 10 7 1 97 9.5 10.5 6.5 7.5 NA 0.1 1
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1572 #FFC9BB 12 7 1 117 11.5 12.5 6.5 7.5 NA 0.1 1
1573 #FF8976 13 7 1 127 12.5 13.5 6.5 7.5 NA 0.1 1
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1594 #FF816C 20 7 1 197 19.5 20.5 6.5 7.5 NA 0.1 1
1595 #FFBBBB 20 7 1 197 19.5 20.5 6.5 7.5 NA 0.1 1
1596 #BBCCFF 20 7 1 197 19.5 20.5 6.5 7.5 NA 0.1 1
1597 #FF7F69 21 7 1 207 20.5 21.5 6.5 7.5 NA 0.1 1
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1609 #FF806B 25 7 1 247 24.5 25.5 6.5 7.5 NA 0.1 1
1610 #FFB3AF 25 7 1 247 24.5 25.5 6.5 7.5 NA 0.1 1
1611 #FFC1BB 25 7 1 247 24.5 25.5 6.5 7.5 NA 0.1 1
1612 #FF806A 26 7 1 257 25.5 26.5 6.5 7.5 NA 0.1 1
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1614 #FFC1BB 26 7 1 257 25.5 26.5 6.5 7.5 NA 0.1 1
1615 #FF7E68 27 7 1 267 26.5 27.5 6.5 7.5 NA 0.1 1
1616 #FFB2AE 27 7 1 267 26.5 27.5 6.5 7.5 NA 0.1 1
1617 #FFC1BB 27 7 1 267 26.5 27.5 6.5 7.5 NA 0.1 1
1618 #FF7C66 28 7 1 277 27.5 28.5 6.5 7.5 NA 0.1 1
1619 #FFB2AD 28 7 1 277 27.5 28.5 6.5 7.5 NA 0.1 1
1620 #FFC2BB 28 7 1 277 27.5 28.5 6.5 7.5 NA 0.1 1
1621 #FF7B64 29 7 1 287 28.5 29.5 6.5 7.5 NA 0.1 1
1622 #FFB1AD 29 7 1 287 28.5 29.5 6.5 7.5 NA 0.1 1
1623 #FFC5BB 29 7 1 287 28.5 29.5 6.5 7.5 NA 0.1 1
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1646 #FFC0BB 5 8 1 48 4.5 5.5 7.5 8.5 NA 0.1 1
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1650 #FFC9BB 6 8 1 58 5.5 6.5 7.5 8.5 NA 0.1 1
1651 #FF8875 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
1652 #FFBFBB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
1653 #FFCABB 7 8 1 68 6.5 7.5 7.5 8.5 NA 0.1 1
1654 #FF8672 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
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1656 #FFCCBB 8 8 1 78 7.5 8.5 7.5 8.5 NA 0.1 1
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1658 #FFBFBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
1659 #FFCBBB 9 8 1 88 8.5 9.5 7.5 8.5 NA 0.1 1
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1661 #FFBEBB 10 8 1 98 9.5 10.5 7.5 8.5 NA 0.1 1
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1665 #FFC9BB 11 8 1 108 10.5 11.5 7.5 8.5 NA 0.1 1
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1668 #FFC8BB 12 8 1 118 11.5 12.5 7.5 8.5 NA 0.1 1
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1671 #FFC8BB 13 8 1 128 12.5 13.5 7.5 8.5 NA 0.1 1
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1673 #FFBDBB 14 8 1 138 13.5 14.5 7.5 8.5 NA 0.1 1
1674 #FFC8BB 14 8 1 138 13.5 14.5 7.5 8.5 NA 0.1 1
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1688 #FFBBBB 19 8 1 188 18.5 19.5 7.5 8.5 NA 0.1 1
1689 #FFC7BB 19 8 1 188 18.5 19.5 7.5 8.5 NA 0.1 1
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1700 #FFB2AE 23 8 1 228 22.5 23.5 7.5 8.5 NA 0.1 1
1701 #FFC1BB 23 8 1 228 22.5 23.5 7.5 8.5 NA 0.1 1
1702 #FF7D67 24 8 1 238 23.5 24.5 7.5 8.5 NA 0.1 1
1703 #FFB2AE 24 8 1 238 23.5 24.5 7.5 8.5 NA 0.1 1
1704 #FFC1BB 24 8 1 238 23.5 24.5 7.5 8.5 NA 0.1 1
1705 #FF7B65 25 8 1 248 24.5 25.5 7.5 8.5 NA 0.1 1
1706 #FFB1AD 25 8 1 248 24.5 25.5 7.5 8.5 NA 0.1 1
1707 #FFC1BB 25 8 1 248 24.5 25.5 7.5 8.5 NA 0.1 1
1708 #FF7962 26 8 1 258 25.5 26.5 7.5 8.5 NA 0.1 1
1709 #FFB0AB 26 8 1 258 25.5 26.5 7.5 8.5 NA 0.1 1
1710 #FFC1BB 26 8 1 258 25.5 26.5 7.5 8.5 NA 0.1 1
1711 #FF7760 27 8 1 268 26.5 27.5 7.5 8.5 NA 0.1 1
1712 #FFB0AB 27 8 1 268 26.5 27.5 7.5 8.5 NA 0.1 1
1713 #FFC1BB 27 8 1 268 26.5 27.5 7.5 8.5 NA 0.1 1
1714 #FF755D 28 8 1 278 27.5 28.5 7.5 8.5 NA 0.1 1
1715 #FFAFAA 28 8 1 278 27.5 28.5 7.5 8.5 NA 0.1 1
1716 #FFC2BB 28 8 1 278 27.5 28.5 7.5 8.5 NA 0.1 1
1717 #FF735C 29 8 1 288 28.5 29.5 7.5 8.5 NA 0.1 1
1718 #FFAFAA 29 8 1 288 28.5 29.5 7.5 8.5 NA 0.1 1
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1721 #FFAFAA 30 8 1 298 29.5 30.5 7.5 8.5 NA 0.1 1
1722 #FFC5BB 30 8 1 298 29.5 30.5 7.5 8.5 NA 0.1 1
1723 #FF725B 31 8 1 308 30.5 31.5 7.5 8.5 NA 0.1 1
1724 #FFAFAA 31 8 1 308 30.5 31.5 7.5 8.5 NA 0.1 1
1725 #FFC5BB 31 8 1 308 30.5 31.5 7.5 8.5 NA 0.1 1
1726 #FF725B 32 8 1 318 31.5 32.5 7.5 8.5 NA 0.1 1
1727 #FFAFAA 32 8 1 318 31.5 32.5 7.5 8.5 NA 0.1 1
1728 #FFC5BB 32 8 1 318 31.5 32.5 7.5 8.5 NA 0.1 1
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1742 #FFC0BB 5 9 1 49 4.5 5.5 8.5 9.5 NA 0.1 1
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1745 #FFBFBB 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
1746 #BBCAFF 6 9 1 59 5.5 6.5 8.5 9.5 NA 0.1 1
1747 #FF8571 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
1748 #FFBFBB 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
1749 #BBC9FF 7 9 1 69 6.5 7.5 8.5 9.5 NA 0.1 1
1750 #FF826E 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1751 #FFBFBB 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1752 #BBC6FF 8 9 1 79 7.5 8.5 8.5 9.5 NA 0.1 1
1753 #FF826E 9 9 1 89 8.5 9.5 8.5 9.5 NA 0.1 1
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1761 #BBCAFF 11 9 1 109 10.5 11.5 8.5 9.5 NA 0.1 1
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1767 #FFC9BB 13 9 1 129 12.5 13.5 8.5 9.5 NA 0.1 1
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1809 #FFC6BB 27 9 1 269 26.5 27.5 8.5 9.5 NA 0.1 1
1810 #FF6E55 28 9 1 279 27.5 28.5 8.5 9.5 NA 0.1 1
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1813 #FF6C54 29 9 1 289 28.5 29.5 8.5 9.5 NA 0.1 1
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1820 #FFAEA8 31 9 1 309 30.5 31.5 8.5 9.5 NA 0.1 1
1821 #FFCABB 31 9 1 309 30.5 31.5 8.5 9.5 NA 0.1 1
1822 #FF6B52 32 9 1 319 31.5 32.5 8.5 9.5 NA 0.1 1
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1824 #FFCABB 32 9 1 319 31.5 32.5 8.5 9.5 NA 0.1 1
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1831 #FFA8A0 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
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1839 #BBCBFF 5 10 1 50 4.5 5.5 9.5 10.5 NA 0.1 1
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1841 #FFBFBB 6 10 1 60 5.5 6.5 9.5 10.5 NA 0.1 1
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1871 #FFBDBB 16 10 1 160 15.5 16.5 9.5 10.5 NA 0.1 1
1872 #FFC9BB 16 10 1 160 15.5 16.5 9.5 10.5 NA 0.1 1
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1875 #FFCABB 17 10 1 170 16.5 17.5 9.5 10.5 NA 0.1 1
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1878 #FFCCBB 18 10 1 180 17.5 18.5 9.5 10.5 NA 0.1 1
1879 #FF7962 19 10 1 190 18.5 19.5 9.5 10.5 NA 0.1 1
1880 #FFBBBB 19 10 1 190 18.5 19.5 9.5 10.5 NA 0.1 1
1881 #BBCCFF 19 10 1 190 18.5 19.5 9.5 10.5 NA 0.1 1
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1883 #FFB7B6 20 10 1 200 19.5 20.5 9.5 10.5 NA 0.1 1
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1885 #FF6F57 21 10 1 210 20.5 21.5 9.5 10.5 NA 0.1 1
1886 #FFB4B0 21 10 1 210 20.5 21.5 9.5 10.5 NA 0.1 1
1887 #BBCCFF 21 10 1 210 20.5 21.5 9.5 10.5 NA 0.1 1
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1889 #FFB0AA 22 10 1 220 21.5 22.5 9.5 10.5 NA 0.1 1
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1891 #FF654C 23 10 1 230 22.5 23.5 9.5 10.5 NA 0.1 1
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1893 #FFC6BB 23 10 1 230 22.5 23.5 9.5 10.5 NA 0.1 1
1894 #FF5D43 24 10 1 240 23.5 24.5 9.5 10.5 NA 0.1 1
1895 #FFA9A2 24 10 1 240 23.5 24.5 9.5 10.5 NA 0.1 1
1896 #FFC6BB 24 10 1 240 23.5 24.5 9.5 10.5 NA 0.1 1
1897 #FF5339 25 10 1 250 24.5 25.5 9.5 10.5 NA 0.1 1
1898 #FFA79E 25 10 1 250 24.5 25.5 9.5 10.5 NA 0.1 1
1899 #FFC6BB 25 10 1 250 24.5 25.5 9.5 10.5 NA 0.1 1
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1901 #FFA49A 26 10 1 260 25.5 26.5 9.5 10.5 NA 0.1 1
1902 #FFC6BB 26 10 1 260 25.5 26.5 9.5 10.5 NA 0.1 1
1903 #FF301A 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1904 #FF9F94 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1905 #FFC6BB 27 10 1 270 26.5 27.5 9.5 10.5 NA 0.1 1
1906 #FF1106 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1907 #FF9C8F 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1908 #FFC7BB 28 10 1 280 27.5 28.5 9.5 10.5 NA 0.1 1
1909 #FF0B03 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1910 #FF9C8F 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1911 #FFC9BB 29 10 1 290 28.5 29.5 9.5 10.5 NA 0.1 1
1912 #FF0100 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1913 #FF9C8F 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1914 #FFCABB 30 10 1 300 29.5 30.5 9.5 10.5 NA 0.1 1
1915 #FF0000 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1916 #FF9B8F 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1917 #FFCABB 31 10 1 310 30.5 31.5 9.5 10.5 NA 0.1 1
1918 #FF0000 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1919 #FF9B8F 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1920 #FFCABB 32 10 1 320 31.5 32.5 9.5 10.5 NA 0.1 1
1921 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1922 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1923 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
1924 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1925 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1926 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
1927 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1928 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1929 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
1930 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1931 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1932 #BBCBFF 4 1 1 31 3.5 4.5 0.5 1.5 NA 0.1 1
1933 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1934 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1935 #BBCBFF 5 1 1 41 4.5 5.5 0.5 1.5 NA 0.1 1
1936 #BBCCFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1937 #BBCBFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1938 #BBCAFF 6 1 1 51 5.5 6.5 0.5 1.5 NA 0.1 1
1939 #FFCABB 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1940 #BBCBFF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1941 #BBC6FF 7 1 1 61 6.5 7.5 0.5 1.5 NA 0.1 1
1942 #FFC9BB 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
1943 #BBCBFF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
1944 #BBC5FF 8 1 1 71 7.5 8.5 0.5 1.5 NA 0.1 1
1945 #FFC9BB 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
1946 #BBCBFF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
1947 #BBC5FF 9 1 1 81 8.5 9.5 0.5 1.5 NA 0.1 1
1948 #FFC9BB 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
1949 #BBCBFF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
1950 #BBC5FF 10 1 1 91 9.5 10.5 0.5 1.5 NA 0.1 1
1951 #BBCBFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
1952 #BBCBFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
1953 #BBCBFF 11 1 1 101 10.5 11.5 0.5 1.5 NA 0.1 1
1954 #BBCBFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
1955 #BBCBFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
1956 #BBCBFF 12 1 1 111 11.5 12.5 0.5 1.5 NA 0.1 1
1957 #BBCBFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
1958 #BBCBFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
1959 #BBCBFF 13 1 1 121 12.5 13.5 0.5 1.5 NA 0.1 1
1960 #BBCBFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
1961 #BBCBFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
1962 #BBCBFF 14 1 1 131 13.5 14.5 0.5 1.5 NA 0.1 1
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1964 #BBCBFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
1965 #BBCBFF 15 1 1 141 14.5 15.5 0.5 1.5 NA 0.1 1
1966 #BBCBFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
1967 #BBCBFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
1968 #BBCBFF 16 1 1 151 15.5 16.5 0.5 1.5 NA 0.1 1
1969 #BBCCFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
1970 #BBCBFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
1971 #BBCAFF 17 1 1 161 16.5 17.5 0.5 1.5 NA 0.1 1
1972 #FFC9BB 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
1973 #BBCBFF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
1974 #BBC5FF 18 1 1 171 17.5 18.5 0.5 1.5 NA 0.1 1
1975 #FFC9BB 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
1976 #BBCBFF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
1977 #BBC5FF 19 1 1 181 18.5 19.5 0.5 1.5 NA 0.1 1
1978 #FFC9BB 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
1979 #BBCBFF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
1980 #BBC5FF 20 1 1 191 19.5 20.5 0.5 1.5 NA 0.1 1
1981 #FFC9BB 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
1982 #BBCBFF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
1983 #BBC5FF 21 1 1 201 20.5 21.5 0.5 1.5 NA 0.1 1
1984 #BBCBFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
1985 #BBCBFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
1986 #BBCBFF 22 1 1 211 21.5 22.5 0.5 1.5 NA 0.1 1
1987 #BBCBFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
1988 #BBCBFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
1989 #BBCBFF 23 1 1 221 22.5 23.5 0.5 1.5 NA 0.1 1
1990 #BBCBFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
1991 #BBCBFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
1992 #BBCBFF 24 1 1 231 23.5 24.5 0.5 1.5 NA 0.1 1
1993 #BBCBFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
1994 #BBCBFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
1995 #BBCBFF 25 1 1 241 24.5 25.5 0.5 1.5 NA 0.1 1
1996 #BBCBFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
1997 #BBCBFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
1998 #BBCBFF 26 1 1 251 25.5 26.5 0.5 1.5 NA 0.1 1
1999 #FFCCBB 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
2000 #BBCBFF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
2001 #BBCAFF 27 1 1 261 26.5 27.5 0.5 1.5 NA 0.1 1
2002 #FFCBBB 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
2003 #BBCBFF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
2004 #BBC8FF 28 1 1 271 27.5 28.5 0.5 1.5 NA 0.1 1
2005 #FFC9BB 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
2006 #BBCBFF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
2007 #BBC5FF 29 1 1 281 28.5 29.5 0.5 1.5 NA 0.1 1
2008 #FFC9BB 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
2009 #BBCBFF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
2010 #BBC5FF 30 1 1 291 29.5 30.5 0.5 1.5 NA 0.1 1
2011 #FFC9BB 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
2012 #BBCBFF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
2013 #BBC5FF 31 1 1 301 30.5 31.5 0.5 1.5 NA 0.1 1
2014 #FFC9BB 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
2015 #BBCBFF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
2016 #BBC5FF 32 1 1 311 31.5 32.5 0.5 1.5 NA 0.1 1
2017 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
2018 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
2019 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
2020 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
2021 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
2022 #BBCBFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
2023 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
2024 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
2025 #BBCBFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
2026 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
2027 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
2028 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
2029 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
2030 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
2031 #BBCBFF 5 2 1 42 4.5 5.5 1.5 2.5 NA 0.1 1
2032 #FFCCBB 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
2033 #BBCBFF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
2034 #BBC9FF 6 2 1 52 5.5 6.5 1.5 2.5 NA 0.1 1
2035 #FFCABB 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
2036 #BBCBFF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
2037 #BBC6FF 7 2 1 62 6.5 7.5 1.5 2.5 NA 0.1 1
2038 #FFC9BB 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
2039 #BBCBFF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
2040 #BBC5FF 8 2 1 72 7.5 8.5 1.5 2.5 NA 0.1 1
2041 #FFC9BB 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
2042 #BBCBFF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
2043 #BBC4FF 9 2 1 82 8.5 9.5 1.5 2.5 NA 0.1 1
2044 #FFC9BB 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
2045 #BBCBFF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
2046 #BBC4FF 10 2 1 92 9.5 10.5 1.5 2.5 NA 0.1 1
2047 #FFC9BB 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
2048 #BBCBFF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
2049 #BBC4FF 11 2 1 102 10.5 11.5 1.5 2.5 NA 0.1 1
2050 #FFC9BB 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
2051 #BBCBFF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
2052 #BBC4FF 12 2 1 112 11.5 12.5 1.5 2.5 NA 0.1 1
2053 #FFC9BB 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
2054 #BBCBFF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
2055 #BBC4FF 13 2 1 122 12.5 13.5 1.5 2.5 NA 0.1 1
2056 #FFC9BB 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
2057 #BBCBFF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
2058 #BBC4FF 14 2 1 132 13.5 14.5 1.5 2.5 NA 0.1 1
2059 #FFC9BB 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
2060 #BBCBFF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
2061 #BBC4FF 15 2 1 142 14.5 15.5 1.5 2.5 NA 0.1 1
2062 #FFC9BB 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
2063 #BBCBFF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
2064 #BBC4FF 16 2 1 152 15.5 16.5 1.5 2.5 NA 0.1 1
2065 #FFC8BB 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
2066 #BBCBFF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
2067 #BBC3FF 17 2 1 162 16.5 17.5 1.5 2.5 NA 0.1 1
2068 #FFC5BB 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
2069 #BBCBFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
2070 #BBBEFF 18 2 1 172 17.5 18.5 1.5 2.5 NA 0.1 1
2071 #FFC5BB 19 2 1 182 18.5 19.5 1.5 2.5 NA 0.1 1
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2074 #FFC5BB 20 2 1 192 19.5 20.5 1.5 2.5 NA 0.1 1
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2077 #FFC5BB 21 2 1 202 20.5 21.5 1.5 2.5 NA 0.1 1
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2080 #FFC8BB 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
2081 #BBCBFF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
2082 #BBC4FF 22 2 1 212 21.5 22.5 1.5 2.5 NA 0.1 1
2083 #FFC8BB 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
2084 #BBCBFF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
2085 #BBC4FF 23 2 1 222 22.5 23.5 1.5 2.5 NA 0.1 1
2086 #FFC8BB 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
2087 #BBCBFF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
2088 #BBC4FF 24 2 1 232 23.5 24.5 1.5 2.5 NA 0.1 1
2089 #FFC8BB 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
2090 #BBCBFF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
2091 #BBC4FF 25 2 1 242 24.5 25.5 1.5 2.5 NA 0.1 1
2092 #FFC8BB 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
2093 #BBCBFF 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
2094 #BBC4FF 26 2 1 252 25.5 26.5 1.5 2.5 NA 0.1 1
2095 #FFC8BB 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
2096 #BBCBFF 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
2097 #BBC2FF 27 2 1 262 26.5 27.5 1.5 2.5 NA 0.1 1
2098 #FFC7BB 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
2099 #BBCBFF 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
2100 #BBC1FF 28 2 1 272 27.5 28.5 1.5 2.5 NA 0.1 1
2101 #FFC5BB 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
2102 #BBCBFF 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
2103 #BBBEFF 29 2 1 282 28.5 29.5 1.5 2.5 NA 0.1 1
2104 #FFC5BB 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
2105 #BBCBFF 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
2106 #BBBDFF 30 2 1 292 29.5 30.5 1.5 2.5 NA 0.1 1
2107 #FFC5BB 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
2108 #BBCBFF 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
2109 #BBBDFF 31 2 1 302 30.5 31.5 1.5 2.5 NA 0.1 1
2110 #FFC5BB 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
2111 #BBCBFF 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
2112 #BBBDFF 32 2 1 312 31.5 32.5 1.5 2.5 NA 0.1 1
2113 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
2114 #BBCBFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
2115 #BBC3FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
2116 #FFC8BB 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
2117 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
2118 #BBC3FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
2119 #FFC8BB 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
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2789 NA NA NA
2790 NA NA NA
2791 NA NA NA
2792 NA NA NA
2793 NA NA NA
2794 NA NA NA
2795 NA NA NA
2796 NA NA NA
2797 NA NA NA
2798 NA NA NA
2799 NA NA NA
2800 NA NA NA
2801 NA NA NA
2802 NA NA NA
2803 NA NA NA
2804 NA NA NA
2805 NA NA NA
2806 NA NA NA
2807 NA NA NA
2808 NA NA NA
2809 NA NA NA
2810 NA NA NA
2811 NA NA NA
2812 NA NA NA
2813 NA NA NA
2814 NA NA NA
2815 NA NA NA
2816 NA NA NA
2817 NA NA NA
2818 NA NA NA
2819 NA NA NA
2820 NA NA NA
2821 NA NA NA
2822 NA NA NA
2823 NA NA NA
2824 NA NA NA
2825 NA NA NA
2826 NA NA NA
2827 NA NA NA
2828 NA NA NA
2829 NA NA NA
2830 NA NA NA
2831 NA NA NA
2832 NA NA NA
2833 NA NA NA
2834 NA NA NA
2835 NA NA NA
2836 NA NA NA
2837 NA NA NA
2838 NA NA NA
2839 NA NA NA
2840 NA NA NA
2841 NA NA NA
2842 NA NA NA
2843 NA NA NA
2844 NA NA NA
2845 NA NA NA
2846 NA NA NA
2847 NA NA NA
2848 NA NA NA
2849 NA NA NA
2850 NA NA NA
2851 NA NA NA
2852 NA NA NA
2853 NA NA NA
2854 NA NA NA
2855 NA NA NA
2856 NA NA NA
2857 NA NA NA
2858 NA NA NA
2859 NA NA NA
2860 NA NA NA
2861 NA NA NA
2862 NA NA NA
2863 NA NA NA
2864 NA NA NA
2865 NA NA NA
2866 NA NA NA
2867 NA NA NA
2868 NA NA NA
2869 NA NA NA
2870 NA NA NA
2871 NA NA NA
2872 NA NA NA
2873 NA NA NA
2874 NA NA NA
2875 NA NA NA
2876 NA NA NA
2877 NA NA NA
2878 NA NA NA
2879 NA NA NA
2880 NA NA NA
$.all_cats$d2$`gear:carb`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype alpha
1 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
2 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
3 #BBCBFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
4 #FFCABB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
5 #BBCBFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
6 #BBC8FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
7 #FFCABB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
8 #BBCBFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
9 #BBC8FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
10 #FFC5BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
11 #BBCBFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
12 #BBC5FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
13 #BBC9FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
14 #BBC6FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
15 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
16 #BBCCFF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
17 #BBC7FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
18 #BBC4FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
19 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
20 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
22 #BBC6FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
23 #BBC4FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
24 #BBC3FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
25 #BBCAFF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
26 #BBC6FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
27 #BBC3FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
28 #FFC6BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
29 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
30 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
31 #BBC2FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
32 #BBBCFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
33 #B7B4FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
34 #BBC1FF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
35 #BBBDFF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
36 #B7B4FF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
37 #FFCABB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
38 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
39 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
40 #BBBFFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
41 #BBBCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
42 #B8B5FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
43 #BBC1FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
44 #BBBDFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
45 #B7B4FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
46 #FFC3BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
47 #BBCCFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
48 #BBC7FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
49 #FFBEBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
50 #BBCCFF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
51 #BBC4FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
52 #FFBEBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
53 #BBCCFF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
54 #BBC4FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
55 #FFB5B2 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
56 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
57 #BBC1FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
58 #FFB5B2 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
59 #BBCCFF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
60 #BBC1FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
61 #FFC6BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
62 #BBCAFF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
63 #BBC5FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
64 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
65 #BBCCFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
66 #BBC4FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
67 #FFBEBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
68 #BBCCFF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
69 #BBC4FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
70 #FFBEBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
71 #BBCAFF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
72 #BBC0FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
73 #FFB5B2 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
74 #BBCCFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
75 #BBC2FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
76 #FFC3BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
77 #BBCCFF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
78 #BBC7FF 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
79 #FFC3BB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
80 #BBCAFF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
81 #BBC3FF 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
82 #FFBEBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
83 #BBCCFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
84 #BBC4FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
85 #FFB4B1 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
86 #BBCCFF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
87 #BBC1FF 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
88 #FFBDBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
89 #BBCAFF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
90 #BBC0FF 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
91 #FFC2BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
92 #FFC9BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
93 #BBC8FF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1 NA
94 #FFBEBB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
95 #FFC9BB 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
96 #BBC6FF 2 1 1 6 1.5 2.5 0.5 1.5 NA 0.1 1 NA
97 #FFBDBB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
98 #FFC9BB 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
99 #BBC6FF 3 1 1 11 2.5 3.5 0.5 1.5 NA 0.1 1 NA
100 #FFB2AF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
101 #FFC9BB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
102 #BBC3FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1 NA
103 #FF826D 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
104 #FFC0BB 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
105 #BBC4FF 2 2 1 7 1.5 2.5 1.5 2.5 NA 0.1 1 NA
106 #FFA69D 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
107 #FFC0BB 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
108 #BBC9FF 3 2 1 12 2.5 3.5 1.5 2.5 NA 0.1 1 NA
109 #FFBEBB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
110 #FFC8BB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
111 #BBC6FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1 NA
112 #FF8976 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
113 #FFBDBB 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
114 #BBC8FF 2 3 1 8 1.5 2.5 2.5 3.5 NA 0.1 1 NA
115 #FF745D 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
116 #FFBDBB 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
117 #BBC5FF 3 3 1 13 2.5 3.5 2.5 3.5 NA 0.1 1 NA
118 #FFB2AE 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
119 #FFC7BB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
120 #BBC4FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1 NA
121 #FF593F 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
122 #FF8A78 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
123 #FFC2BB 2 4 1 9 1.5 2.5 3.5 4.5 NA 0.1 1 NA
124 #FF3A23 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
125 #FF8976 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
126 #FFC7BB 3 4 1 14 2.5 3.5 3.5 4.5 NA 0.1 1 NA
127 #FFBDBB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
128 #FFC7BB 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
129 #BBC7FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1 NA
130 #FF1508 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
131 #FF8B78 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
132 #FFCCBB 2 5 1 10 1.5 2.5 4.5 5.5 NA 0.1 1 NA
133 #FF0000 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
134 #FF8976 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
135 #FFCCBB 3 5 1 15 2.5 3.5 4.5 5.5 NA 0.1 1 NA
width height
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
101 NA NA
102 NA NA
103 NA NA
104 NA NA
105 NA NA
106 NA NA
107 NA NA
108 NA NA
109 NA NA
110 NA NA
111 NA NA
112 NA NA
113 NA NA
114 NA NA
115 NA NA
116 NA NA
117 NA NA
118 NA NA
119 NA NA
120 NA NA
121 NA NA
122 NA NA
123 NA NA
124 NA NA
125 NA NA
126 NA NA
127 NA NA
128 NA NA
129 NA NA
130 NA NA
131 NA NA
132 NA NA
133 NA NA
134 NA NA
135 NA NA
$.all_cats$d2$`gear:wt`
fill x y PANEL group xmin xmax ymin ymax colour linewidth linetype
1 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
2 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
3 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
4 #BBCBFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
5 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
6 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
7 #BBCBFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
8 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
9 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
10 #BBCCFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
11 #BBCAFF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
12 #BBC8FF 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
13 #BBCCFF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
14 #BBC9FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
15 #BBC8FF 2 2 1 12 1.5 2.5 1.5 2.5 NA 0.1 1
16 #BBCCFF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
17 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
18 #BBC8FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
19 #FFCABB 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
20 #BBCAFF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
21 #BBC8FF 1 3 1 3 0.5 1.5 2.5 3.5 NA 0.1 1
22 #BBCBFF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
23 #BBC9FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
24 #BBC7FF 2 3 1 13 1.5 2.5 2.5 3.5 NA 0.1 1
25 #BBCAFF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
26 #BBC8FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
27 #BBC7FF 3 3 1 23 2.5 3.5 2.5 3.5 NA 0.1 1
28 #FFCABB 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
29 #BBCAFF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
30 #BBC8FF 1 4 1 4 0.5 1.5 3.5 4.5 NA 0.1 1
31 #BBCAFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
32 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
33 #BBC6FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
34 #BBC9FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
35 #BBC7FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
36 #BBC6FF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
37 #BBCBFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
38 #BBCAFF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
39 #BBC8FF 1 5 1 5 0.5 1.5 4.5 5.5 NA 0.1 1
40 #BBC8FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
41 #BBC4FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
42 #BBC0FF 2 5 1 15 1.5 2.5 4.5 5.5 NA 0.1 1
43 #BBC7FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
44 #BBC3FF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
45 #BBBFFF 3 5 1 25 2.5 3.5 4.5 5.5 NA 0.1 1
46 #FFCABB 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
47 #BBCAFF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
48 #BBC8FF 1 6 1 6 0.5 1.5 5.5 6.5 NA 0.1 1
49 #BBC9FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
50 #BBC4FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
51 #BBC0FF 2 6 1 16 1.5 2.5 5.5 6.5 NA 0.1 1
52 #BBC7FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
53 #BBC3FF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
54 #BBBFFF 3 6 1 26 2.5 3.5 5.5 6.5 NA 0.1 1
55 #FFCABB 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
56 #BBCAFF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
57 #BBC8FF 1 7 1 7 0.5 1.5 6.5 7.5 NA 0.1 1
58 #BBC9FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
59 #BBC4FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
60 #BBC0FF 2 7 1 17 1.5 2.5 6.5 7.5 NA 0.1 1
61 #BBC6FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
62 #BBC3FF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
63 #BBBFFF 3 7 1 27 2.5 3.5 6.5 7.5 NA 0.1 1
64 #BBCBFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
65 #BBCAFF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
66 #BBC8FF 1 8 1 8 0.5 1.5 7.5 8.5 NA 0.1 1
67 #BBC7FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
68 #BBC4FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
69 #BBC0FF 2 8 1 18 1.5 2.5 7.5 8.5 NA 0.1 1
70 #BBC6FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
71 #BBC3FF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
72 #BBBFFF 3 8 1 28 2.5 3.5 7.5 8.5 NA 0.1 1
73 #FFCABB 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
74 #BBCAFF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
75 #BBC8FF 1 9 1 9 0.5 1.5 8.5 9.5 NA 0.1 1
76 #BBC9FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
77 #BBC4FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
78 #BBC0FF 2 9 1 19 1.5 2.5 8.5 9.5 NA 0.1 1
79 #BBC7FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
80 #BBC3FF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
81 #BBBFFF 3 9 1 29 2.5 3.5 8.5 9.5 NA 0.1 1
82 #FFCABB 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
83 #BBCAFF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
84 #BBC8FF 1 10 1 10 0.5 1.5 9.5 10.5 NA 0.1 1
85 #BBC9FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
86 #BBC4FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
87 #BBC0FF 2 10 1 20 1.5 2.5 9.5 10.5 NA 0.1 1
88 #BBC7FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
89 #BBC3FF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
90 #BBBFFF 3 10 1 30 2.5 3.5 9.5 10.5 NA 0.1 1
91 #FFC3BB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
92 #FFCCBB 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
93 #BBCAFF 1 1 1 1 0.5 1.5 0.5 1.5 NA 0.1 1
94 #FFC1BB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
95 #FFCCBB 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
96 #BBCAFF 2 1 1 11 1.5 2.5 0.5 1.5 NA 0.1 1
97 #FFC0BB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
98 #FFCCBB 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
99 #BBCAFF 3 1 1 21 2.5 3.5 0.5 1.5 NA 0.1 1
100 #FFBDBB 1 2 1 2 0.5 1.5 1.5 2.5 NA 0.1 1
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22 #FFC8BB 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
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24 #BBC9FF 3 2 1 22 2.5 3.5 1.5 2.5 NA 0.1 1
25 #FFC2BB 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
26 #BBCBFF 4 2 1 32 3.5 4.5 1.5 2.5 NA 0.1 1
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50 #BBCBFF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
51 #BBC8FF 2 4 1 14 1.5 2.5 3.5 4.5 NA 0.1 1
52 #BBCBFF 3 4 1 24 2.5 3.5 3.5 4.5 NA 0.1 1
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$.all_cats$eff
NULL
Code
capture.output(print(cat_cars_ale))
Message
<ALE> object of a <multinom/nnet> model that predicts `continent` (a
categorical outcome) from a 64-row by 8-column dataset.
ALE data, statistics, surrogate p-values, and raw bootstrap data are provided
for the following terms:
7 1D terms: mpg, vs, am, model, gear, carb, and wt
21 2D terms: mpg:vs, mpg:am, mpg:model, mpg:gear, mpg:carb, mpg:wt, vs:am,
vs:model, vs:gear, vs:carb, vs:wt, am:model, am:gear, am:carb, am:wt,
model:gear, model:carb, model:wt, ..., gear:wt, and carb:wt
The results were bootstrapped with 2 iterations.
Output
character(0)
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