change: Dissimilarities, distances and rates of change
In paleoMAS: Paleoecological Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function calculates dissimilarity or distances between contiguous samples (timeslices), as well as rates of ecological change when a robust age model is available.
Usage
1
2
Arguments
x
A matrix with samples in rows and species in columns.
age
Vector with sample ages.
dca
Logical indicating whether or not detrended correspondece analysis scores (DCA, performed according to decorana package vegan) will be used as a basis for dissimilarity computation. If dca=TRUE the dissimilarity between samples is calculated as euclidean distance between the first four DCA axis scores. If FALSE dissimilarity is calculated according to the methods specified in meth).
meth
Corresponds to methods available in vegdist of vegan. Available methods are "manhattan",
"euclidean", "canberra", "bray", "kulczynski",
"jaccard", "gower", "morisita", "horn",
"mountford", "raup" , "binomial" or "chao". See vegdist (package vegan) for details.
bin
Argument of the function vegdist (package vegan) that standardizes the data into presence/absence before calculating the dissimilarity.
roc
Logical argument of whether or not the calculation of rates of change is desired.
digits
Number that specifies the digits desired for the rounded-up ages.
Details
Rates of change are calculated as: RoC[jk] = vegdist[jk]/"res"). For further discussion on assumptions involved, see Urrego et al (2009). DCA is calculated according to decorana (package vegan.)
Value
Returns a matrix with ages and their corresponding dissimilarity, distances or RoC. As calculations between subsequent samples return n-1 observations, the distance, dissimilarity, or RoC are assigned to the youngest of the two contiguous samples. When roc=TRUE, it also returns a vector res with time steps between samples.
Author(s)
Dunia H. Urrego, Alexander Correa-Metrio.
References
Urrego DH, Bush M, Silman MR, Correa-Metrio A, Ledru M-P, Mayle FE, Valencia BG (2009). Millennial-scale ecological changes in tropical South America since the Last Glacial Maximum. Past climate variability from the Last Glacial Maximum to the Holocene in South America and surrounding regions. (eds. Vimeux F, Sylvestre F, Khodri M). Springer.
See Also
See vegdist and decorana for details on dissimilarity indexes.
Examples
1
2
3
4
5
6
7
8
data(quexilchron,quexildepths,quexilper)
ages<-chron(quexilchron,quexildepths,max.depth=1957)$chronology[,2]
#Absolute change
change(quexilper,ages,meth="bray")
change(quexilper,ages,dca=TRUE)
#Rate of change
change(quexilper,ages,meth="bray",roc=TRUE)
change(quexilper,ages,dca=TRUE,roc=TRUE)
Example output
Loading required package: MASS
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-3
Age Distance
1 10262.1 0.23265395
2 10397.1 0.20875219
3 10532.2 0.25666158
4 10667.2 0.45246215
5 11072.3 0.18735637
6 11207.3 0.22947345
7 11342.4 0.39439586
8 11612.4 0.33988198
9 11747.4 0.35621384
10 11882.5 0.15025878
11 11923.0 0.44276892
12 12017.5 0.89596341
13 12380.3 0.28465300
14 12527.9 0.20662383
15 12823.2 0.09220223
16 13118.4 0.10285798
17 13413.7 0.40802750
18 14299.5 0.26668686
19 14890.0 0.26574376
20 15775.8 0.46423428
21 16366.3 0.18628688
22 16956.8 0.40752877
23 17547.4 0.24557109
24 18728.4 0.41462330
25 19319.0 0.44416581
26 20500.0 0.37013518
27 21385.8 0.32646741
28 21976.3 0.24996522
29 23157.4 0.34861869
30 23747.9 0.22301905
31 24633.7 0.36765322
32 25224.2 0.49041299
33 25814.8 0.43198220
34 26405.3 0.49919411
35 27291.1 0.23475015
36 27881.6 0.25133666
37 28472.2 0.38569281
38 29062.7 0.21843440
39 29653.2 0.35506574
40 29948.5 0.36575992
41 30391.4 0.22929998
42 30834.3 0.40402101
43 31129.5 0.27649660
44 31424.8 0.14692531
45 31720.0 0.53525168
46 32310.6 0.62204043
47 32605.8 0.12378553
48 32901.1 0.59056543
49 33196.4 0.36900238
50 33491.6 0.41473160
51 34082.2 0.27325295
52 34377.4 0.24259913
53 35558.5 0.18585806
54 35853.7 0.35096570
55 36444.3 0.26673027
56 36739.5 0.28051344
57 37330.1 0.41285282
58 38215.9 0.32529790
59 38806.4 0.25405797
60 39544.5 0.27002595
61 39839.8 0.31793679
62 40430.3 0.35169897
63 41168.5 0.44220332
64 41759.0 0.41191294
65 41906.7 0.35523301
66 42349.6 0.22143449
67 42940.1 0.36454582
Age Distance
1 10262.1 0.35045855
2 10397.1 0.14414751
3 10532.2 0.23185730
4 10667.2 0.78975455
5 11072.3 0.21300137
6 11207.3 0.38791557
7 11342.4 0.78096478
8 11612.4 0.72602745
9 11747.4 0.38722874
10 11882.5 0.26043240
11 11923.0 1.05520086
12 12017.5 1.75710850
13 12380.3 0.51188395
14 12527.9 0.35145250
15 12823.2 0.06629561
16 13118.4 0.17362939
17 13413.7 0.94208063
18 14299.5 0.25813444
19 14890.0 0.26162932
20 15775.8 0.75455351
21 16366.3 0.18294146
22 16956.8 0.45736171
23 17547.4 0.26888574
24 18728.4 0.76047405
25 19319.0 0.84443354
26 20500.0 0.26661246
27 21385.8 0.34207876
28 21976.3 0.38010375
29 23157.4 0.54274455
30 23747.9 0.31416340
31 24633.7 0.64171059
32 25224.2 0.93290557
33 25814.8 0.71582984
34 26405.3 0.98695377
35 27291.1 0.47217770
36 27881.6 0.37406382
37 28472.2 0.57296484
38 29062.7 0.25939085
39 29653.2 0.71548738
40 29948.5 0.91958413
41 30391.4 0.33248963
42 30834.3 0.67088075
43 31129.5 0.63657062
44 31424.8 0.22181960
45 31720.0 0.54467668
46 32310.6 1.31357015
47 32605.8 0.17683198
48 32901.1 1.14899696
49 33196.4 0.77392996
50 33491.6 0.90702179
51 34082.2 0.48360353
52 34377.4 0.24616856
53 35558.5 0.25492071
54 35853.7 0.61985517
55 36444.3 0.44674754
56 36739.5 0.40181229
57 37330.1 0.78205794
58 38215.9 0.55765043
59 38806.4 0.43510469
60 39544.5 0.60840594
61 39839.8 0.54008887
62 40430.3 0.77008824
63 41168.5 0.77840932
64 41759.0 0.86660468
65 41906.7 0.73818163
66 42349.6 0.54678865
67 42940.1 0.67167892
[[1]]
Age Rate of change
1 10262.1 0.0017233626
2 10397.1 0.0015451680
3 10532.2 0.0019011969
4 10667.2 0.0011169147
5 11072.3 0.0013878250
6 11207.3 0.0016985451
7 11342.4 0.0014607254
8 11612.4 0.0025176443
9 11747.4 0.0026366679
10 11882.5 0.0037100933
11 11923.0 0.0046853854
12 12017.5 0.0024695794
13 12380.3 0.0019285434
14 12527.9 0.0006997082
15 12823.2 0.0003123382
16 13118.4 0.0003483169
17 13413.7 0.0004606316
18 14299.5 0.0004516289
19 14890.0 0.0003000042
20 15775.8 0.0007861715
21 16366.3 0.0003154731
22 16956.8 0.0006900250
23 17547.4 0.0002079349
24 18728.4 0.0007020374
25 19319.0 0.0003760930
26 20500.0 0.0004178541
27 21385.8 0.0005528661
28 21976.3 0.0002116376
29 23157.4 0.0005903788
30 23747.9 0.0002517713
31 24633.7 0.0006226134
32 25224.2 0.0008303640
33 25814.8 0.0007315533
34 26405.3 0.0005635517
35 27291.1 0.0003975447
36 27881.6 0.0004255616
37 28472.2 0.0006531631
38 29062.7 0.0003699143
39 29653.2 0.0012023899
40 29948.5 0.0008258296
41 30391.4 0.0005177240
42 30834.3 0.0013686349
43 31129.5 0.0009363244
44 31424.8 0.0004977145
45 31720.0 0.0009062846
46 32310.6 0.0021071830
47 32605.8 0.0004191857
48 32901.1 0.0019998829
49 33196.4 0.0012500081
50 33491.6 0.0007022208
51 34082.2 0.0009256536
52 34377.4 0.0002054010
53 35558.5 0.0006296005
54 35853.7 0.0005942528
55 36444.3 0.0009035578
56 36739.5 0.0004749635
57 37330.1 0.0004660791
58 38215.9 0.0005508855
59 38806.4 0.0003442053
60 39544.5 0.0009144123
61 39839.8 0.0005384196
62 40430.3 0.0004764278
63 41168.5 0.0007488625
64 41759.0 0.0027888486
65 41906.7 0.0008020614
66 42349.6 0.0003749949
67 42940.1 0.0007263316
[[2]]
2 3 4 5 6 7 8 9 10 11 12
135.0 135.1 135.0 405.1 135.0 135.1 270.0 135.0 135.1 40.5 94.5
13 14 15 16 17 18 19 20 21 22 23
362.8 147.6 295.3 295.2 295.3 885.8 590.5 885.8 590.5 590.5 590.6
24 25 26 27 28 29 30 31 32 33 34
1181.0 590.6 1181.0 885.8 590.5 1181.1 590.5 885.8 590.5 590.6 590.5
35 36 37 38 39 40 41 42 43 44 45
885.8 590.5 590.6 590.5 590.5 295.3 442.9 442.9 295.2 295.3 295.2
46 47 48 49 50 51 52 53 54 55 56
590.6 295.2 295.3 295.3 295.2 590.6 295.2 1181.1 295.2 590.6 295.2
57 58 59 60 61 62 63 64 65 66 67
590.6 885.8 590.5 738.1 295.3 590.5 738.2 590.5 147.7 442.9 590.5
68
501.9
[[1]]
Age Rate of change
1 10262.1 0.0025959893
2 10397.1 0.0010669690
3 10532.2 0.0017174615
4 10667.2 0.0019495299
5 11072.3 0.0015777879
6 11207.3 0.0028713217
7 11342.4 0.0028924622
8 11612.4 0.0053779811
9 11747.4 0.0028662379
10 11882.5 0.0064304297
11 11923.0 0.0111661467
12 12017.5 0.0048431877
13 12380.3 0.0034680484
14 12527.9 0.0011901541
15 12823.2 0.0002245786
16 13118.4 0.0005879763
17 13413.7 0.0010635365
18 14299.5 0.0004371455
19 14890.0 0.0002953594
20 15775.8 0.0012778213
21 16366.3 0.0003098077
22 16956.8 0.0007744018
23 17547.4 0.0002276763
24 18728.4 0.0012876296
25 19319.0 0.0007150157
26 20500.0 0.0003009849
27 21385.8 0.0005793036
28 21976.3 0.0003218218
29 23157.4 0.0009191271
30 23747.9 0.0003546663
31 24633.7 0.0010867241
32 25224.2 0.0015795895
33 25814.8 0.0012122436
34 26405.3 0.0011141948
35 27291.1 0.0007996235
36 27881.6 0.0006333624
37 28472.2 0.0009703046
38 29062.7 0.0004392732
39 29653.2 0.0024229170
40 29948.5 0.0020762794
41 30391.4 0.0007507104
42 30834.3 0.0022726313
43 31129.5 0.0021556743
44 31424.8 0.0007514214
45 31720.0 0.0009222429
46 32310.6 0.0044497634
47 32605.8 0.0005988215
48 32901.1 0.0038909480
49 33196.4 0.0026217140
50 33491.6 0.0015357633
51 34082.2 0.0016382233
52 34377.4 0.0002084231
53 35558.5 0.0008635525
54 35853.7 0.0010495347
55 36444.3 0.0015133724
56 36739.5 0.0006803459
57 37330.1 0.0008828832
58 38215.9 0.0009443699
59 38806.4 0.0005894929
60 39544.5 0.0020602978
61 39839.8 0.0009146298
62 40430.3 0.0010431973
63 41168.5 0.0013182207
64 41759.0 0.0058673303
65 41906.7 0.0016667004
66 42349.6 0.0009259757
67 42940.1 0.0013382724
[[2]]
2 3 4 5 6 7 8 9 10 11 12
135.0 135.1 135.0 405.1 135.0 135.1 270.0 135.0 135.1 40.5 94.5
13 14 15 16 17 18 19 20 21 22 23
362.8 147.6 295.3 295.2 295.3 885.8 590.5 885.8 590.5 590.5 590.6
24 25 26 27 28 29 30 31 32 33 34
1181.0 590.6 1181.0 885.8 590.5 1181.1 590.5 885.8 590.5 590.6 590.5
35 36 37 38 39 40 41 42 43 44 45
885.8 590.5 590.6 590.5 590.5 295.3 442.9 442.9 295.2 295.3 295.2
46 47 48 49 50 51 52 53 54 55 56
590.6 295.2 295.3 295.3 295.2 590.6 295.2 1181.1 295.2 590.6 295.2
57 58 59 60 61 62 63 64 65 66 67
590.6 885.8 590.5 738.1 295.3 590.5 738.2 590.5 147.7 442.9 590.5
68
501.9
paleoMAS documentation built on May 2, 2019, 6:46 a.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function calculates dissimilarity or distances between contiguous samples (timeslices), as well as rates of ecological change when a robust age model is available.
Usage
1 2 |
Arguments
x |
A matrix with samples in rows and species in columns. |
age |
Vector with sample ages. |
dca |
Logical indicating whether or not detrended correspondece analysis scores (DCA, performed according to |
meth |
Corresponds to methods available in |
bin |
Argument of the function |
roc |
Logical argument of whether or not the calculation of rates of change is desired. |
digits |
Number that specifies the digits desired for the rounded-up ages. |
Details
Rates of change are calculated as: RoC[jk] = vegdist[jk]/"res"). For further discussion on assumptions involved, see Urrego et al (2009). DCA is calculated according to decorana (package vegan.)
Value
Returns a matrix with ages and their corresponding dissimilarity, distances or RoC. As calculations between subsequent samples return n-1 observations, the distance, dissimilarity, or RoC are assigned to the youngest of the two contiguous samples. When roc=TRUE, it also returns a vector res with time steps between samples.
Author(s)
Dunia H. Urrego, Alexander Correa-Metrio.
References
Urrego DH, Bush M, Silman MR, Correa-Metrio A, Ledru M-P, Mayle FE, Valencia BG (2009). Millennial-scale ecological changes in tropical South America since the Last Glacial Maximum. Past climate variability from the Last Glacial Maximum to the Holocene in South America and surrounding regions. (eds. Vimeux F, Sylvestre F, Khodri M). Springer.
See Also
See vegdist and decorana for details on dissimilarity indexes.
Examples
1 2 3 4 5 6 7 8 | data(quexilchron,quexildepths,quexilper)
ages<-chron(quexilchron,quexildepths,max.depth=1957)$chronology[,2]
#Absolute change
change(quexilper,ages,meth="bray")
change(quexilper,ages,dca=TRUE)
#Rate of change
change(quexilper,ages,meth="bray",roc=TRUE)
change(quexilper,ages,dca=TRUE,roc=TRUE)
|
Example output
Loading required package: MASS
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-3
Age Distance
1 10262.1 0.23265395
2 10397.1 0.20875219
3 10532.2 0.25666158
4 10667.2 0.45246215
5 11072.3 0.18735637
6 11207.3 0.22947345
7 11342.4 0.39439586
8 11612.4 0.33988198
9 11747.4 0.35621384
10 11882.5 0.15025878
11 11923.0 0.44276892
12 12017.5 0.89596341
13 12380.3 0.28465300
14 12527.9 0.20662383
15 12823.2 0.09220223
16 13118.4 0.10285798
17 13413.7 0.40802750
18 14299.5 0.26668686
19 14890.0 0.26574376
20 15775.8 0.46423428
21 16366.3 0.18628688
22 16956.8 0.40752877
23 17547.4 0.24557109
24 18728.4 0.41462330
25 19319.0 0.44416581
26 20500.0 0.37013518
27 21385.8 0.32646741
28 21976.3 0.24996522
29 23157.4 0.34861869
30 23747.9 0.22301905
31 24633.7 0.36765322
32 25224.2 0.49041299
33 25814.8 0.43198220
34 26405.3 0.49919411
35 27291.1 0.23475015
36 27881.6 0.25133666
37 28472.2 0.38569281
38 29062.7 0.21843440
39 29653.2 0.35506574
40 29948.5 0.36575992
41 30391.4 0.22929998
42 30834.3 0.40402101
43 31129.5 0.27649660
44 31424.8 0.14692531
45 31720.0 0.53525168
46 32310.6 0.62204043
47 32605.8 0.12378553
48 32901.1 0.59056543
49 33196.4 0.36900238
50 33491.6 0.41473160
51 34082.2 0.27325295
52 34377.4 0.24259913
53 35558.5 0.18585806
54 35853.7 0.35096570
55 36444.3 0.26673027
56 36739.5 0.28051344
57 37330.1 0.41285282
58 38215.9 0.32529790
59 38806.4 0.25405797
60 39544.5 0.27002595
61 39839.8 0.31793679
62 40430.3 0.35169897
63 41168.5 0.44220332
64 41759.0 0.41191294
65 41906.7 0.35523301
66 42349.6 0.22143449
67 42940.1 0.36454582
Age Distance
1 10262.1 0.35045855
2 10397.1 0.14414751
3 10532.2 0.23185730
4 10667.2 0.78975455
5 11072.3 0.21300137
6 11207.3 0.38791557
7 11342.4 0.78096478
8 11612.4 0.72602745
9 11747.4 0.38722874
10 11882.5 0.26043240
11 11923.0 1.05520086
12 12017.5 1.75710850
13 12380.3 0.51188395
14 12527.9 0.35145250
15 12823.2 0.06629561
16 13118.4 0.17362939
17 13413.7 0.94208063
18 14299.5 0.25813444
19 14890.0 0.26162932
20 15775.8 0.75455351
21 16366.3 0.18294146
22 16956.8 0.45736171
23 17547.4 0.26888574
24 18728.4 0.76047405
25 19319.0 0.84443354
26 20500.0 0.26661246
27 21385.8 0.34207876
28 21976.3 0.38010375
29 23157.4 0.54274455
30 23747.9 0.31416340
31 24633.7 0.64171059
32 25224.2 0.93290557
33 25814.8 0.71582984
34 26405.3 0.98695377
35 27291.1 0.47217770
36 27881.6 0.37406382
37 28472.2 0.57296484
38 29062.7 0.25939085
39 29653.2 0.71548738
40 29948.5 0.91958413
41 30391.4 0.33248963
42 30834.3 0.67088075
43 31129.5 0.63657062
44 31424.8 0.22181960
45 31720.0 0.54467668
46 32310.6 1.31357015
47 32605.8 0.17683198
48 32901.1 1.14899696
49 33196.4 0.77392996
50 33491.6 0.90702179
51 34082.2 0.48360353
52 34377.4 0.24616856
53 35558.5 0.25492071
54 35853.7 0.61985517
55 36444.3 0.44674754
56 36739.5 0.40181229
57 37330.1 0.78205794
58 38215.9 0.55765043
59 38806.4 0.43510469
60 39544.5 0.60840594
61 39839.8 0.54008887
62 40430.3 0.77008824
63 41168.5 0.77840932
64 41759.0 0.86660468
65 41906.7 0.73818163
66 42349.6 0.54678865
67 42940.1 0.67167892
[[1]]
Age Rate of change
1 10262.1 0.0017233626
2 10397.1 0.0015451680
3 10532.2 0.0019011969
4 10667.2 0.0011169147
5 11072.3 0.0013878250
6 11207.3 0.0016985451
7 11342.4 0.0014607254
8 11612.4 0.0025176443
9 11747.4 0.0026366679
10 11882.5 0.0037100933
11 11923.0 0.0046853854
12 12017.5 0.0024695794
13 12380.3 0.0019285434
14 12527.9 0.0006997082
15 12823.2 0.0003123382
16 13118.4 0.0003483169
17 13413.7 0.0004606316
18 14299.5 0.0004516289
19 14890.0 0.0003000042
20 15775.8 0.0007861715
21 16366.3 0.0003154731
22 16956.8 0.0006900250
23 17547.4 0.0002079349
24 18728.4 0.0007020374
25 19319.0 0.0003760930
26 20500.0 0.0004178541
27 21385.8 0.0005528661
28 21976.3 0.0002116376
29 23157.4 0.0005903788
30 23747.9 0.0002517713
31 24633.7 0.0006226134
32 25224.2 0.0008303640
33 25814.8 0.0007315533
34 26405.3 0.0005635517
35 27291.1 0.0003975447
36 27881.6 0.0004255616
37 28472.2 0.0006531631
38 29062.7 0.0003699143
39 29653.2 0.0012023899
40 29948.5 0.0008258296
41 30391.4 0.0005177240
42 30834.3 0.0013686349
43 31129.5 0.0009363244
44 31424.8 0.0004977145
45 31720.0 0.0009062846
46 32310.6 0.0021071830
47 32605.8 0.0004191857
48 32901.1 0.0019998829
49 33196.4 0.0012500081
50 33491.6 0.0007022208
51 34082.2 0.0009256536
52 34377.4 0.0002054010
53 35558.5 0.0006296005
54 35853.7 0.0005942528
55 36444.3 0.0009035578
56 36739.5 0.0004749635
57 37330.1 0.0004660791
58 38215.9 0.0005508855
59 38806.4 0.0003442053
60 39544.5 0.0009144123
61 39839.8 0.0005384196
62 40430.3 0.0004764278
63 41168.5 0.0007488625
64 41759.0 0.0027888486
65 41906.7 0.0008020614
66 42349.6 0.0003749949
67 42940.1 0.0007263316
[[2]]
2 3 4 5 6 7 8 9 10 11 12
135.0 135.1 135.0 405.1 135.0 135.1 270.0 135.0 135.1 40.5 94.5
13 14 15 16 17 18 19 20 21 22 23
362.8 147.6 295.3 295.2 295.3 885.8 590.5 885.8 590.5 590.5 590.6
24 25 26 27 28 29 30 31 32 33 34
1181.0 590.6 1181.0 885.8 590.5 1181.1 590.5 885.8 590.5 590.6 590.5
35 36 37 38 39 40 41 42 43 44 45
885.8 590.5 590.6 590.5 590.5 295.3 442.9 442.9 295.2 295.3 295.2
46 47 48 49 50 51 52 53 54 55 56
590.6 295.2 295.3 295.3 295.2 590.6 295.2 1181.1 295.2 590.6 295.2
57 58 59 60 61 62 63 64 65 66 67
590.6 885.8 590.5 738.1 295.3 590.5 738.2 590.5 147.7 442.9 590.5
68
501.9
[[1]]
Age Rate of change
1 10262.1 0.0025959893
2 10397.1 0.0010669690
3 10532.2 0.0017174615
4 10667.2 0.0019495299
5 11072.3 0.0015777879
6 11207.3 0.0028713217
7 11342.4 0.0028924622
8 11612.4 0.0053779811
9 11747.4 0.0028662379
10 11882.5 0.0064304297
11 11923.0 0.0111661467
12 12017.5 0.0048431877
13 12380.3 0.0034680484
14 12527.9 0.0011901541
15 12823.2 0.0002245786
16 13118.4 0.0005879763
17 13413.7 0.0010635365
18 14299.5 0.0004371455
19 14890.0 0.0002953594
20 15775.8 0.0012778213
21 16366.3 0.0003098077
22 16956.8 0.0007744018
23 17547.4 0.0002276763
24 18728.4 0.0012876296
25 19319.0 0.0007150157
26 20500.0 0.0003009849
27 21385.8 0.0005793036
28 21976.3 0.0003218218
29 23157.4 0.0009191271
30 23747.9 0.0003546663
31 24633.7 0.0010867241
32 25224.2 0.0015795895
33 25814.8 0.0012122436
34 26405.3 0.0011141948
35 27291.1 0.0007996235
36 27881.6 0.0006333624
37 28472.2 0.0009703046
38 29062.7 0.0004392732
39 29653.2 0.0024229170
40 29948.5 0.0020762794
41 30391.4 0.0007507104
42 30834.3 0.0022726313
43 31129.5 0.0021556743
44 31424.8 0.0007514214
45 31720.0 0.0009222429
46 32310.6 0.0044497634
47 32605.8 0.0005988215
48 32901.1 0.0038909480
49 33196.4 0.0026217140
50 33491.6 0.0015357633
51 34082.2 0.0016382233
52 34377.4 0.0002084231
53 35558.5 0.0008635525
54 35853.7 0.0010495347
55 36444.3 0.0015133724
56 36739.5 0.0006803459
57 37330.1 0.0008828832
58 38215.9 0.0009443699
59 38806.4 0.0005894929
60 39544.5 0.0020602978
61 39839.8 0.0009146298
62 40430.3 0.0010431973
63 41168.5 0.0013182207
64 41759.0 0.0058673303
65 41906.7 0.0016667004
66 42349.6 0.0009259757
67 42940.1 0.0013382724
[[2]]
2 3 4 5 6 7 8 9 10 11 12
135.0 135.1 135.0 405.1 135.0 135.1 270.0 135.0 135.1 40.5 94.5
13 14 15 16 17 18 19 20 21 22 23
362.8 147.6 295.3 295.2 295.3 885.8 590.5 885.8 590.5 590.5 590.6
24 25 26 27 28 29 30 31 32 33 34
1181.0 590.6 1181.0 885.8 590.5 1181.1 590.5 885.8 590.5 590.6 590.5
35 36 37 38 39 40 41 42 43 44 45
885.8 590.5 590.6 590.5 590.5 295.3 442.9 442.9 295.2 295.3 295.2
46 47 48 49 50 51 52 53 54 55 56
590.6 295.2 295.3 295.3 295.2 590.6 295.2 1181.1 295.2 590.6 295.2
57 58 59 60 61 62 63 64 65 66 67
590.6 885.8 590.5 738.1 295.3 590.5 738.2 590.5 147.7 442.9 590.5
68
501.9
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