Nothing
Code
print(fs)
Output
# A tibble: 600 x 14
y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 1
2 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 2
3 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 1
4 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 2
5 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 1
6 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 2
7 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 1
8 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 2
9 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 1
10 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 2
# i 590 more rows
# i 2 more variables: .parameter <chr>, .fitted <dbl>
Code
print(ps)
Output
# A tibble: 600 x 13
y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 1
2 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 2
3 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 1
4 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 2
5 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 1
6 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 2
7 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 1
8 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 2
9 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 1
10 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 2
# i 590 more rows
# i 1 more variable: .response <dbl>
Code
print(ps)
Output
# A tibble: 600 x 13
y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 1
2 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 2
3 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 1
4 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 2
5 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 1
6 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 2
7 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 1
8 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 2
9 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 1
10 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 2
# i 590 more rows
# i 1 more variable: .response <dbl>
Code
print(ss)
Output
# A tibble: 2,400 x 15
y x0 x1 x2 x3 f f0 f1 f2 f3 .row .smooth
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
1 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x0)
2 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x0)
3 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x1)
4 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x1)
5 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x2)
6 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x2)
7 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x3)
8 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x3)
9 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 s(x0)
10 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 s(x0)
# i 2,390 more rows
# i 3 more variables: .term <chr>, .draw <int>, .value <dbl>
Code
print(ss)
Output
# A tibble: 600 x 15
y x0 x1 x2 x3 f f0 f1 f2 f3 .row .smooth
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
1 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x2)
2 10.1 0.786 0.709 0.119 0.975 10.1 1.25 4.13 4.73 0 1 s(x2)
3 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 s(x2)
4 13.5 0.252 0.0412 0.153 0.541 9.33 1.43 1.09 6.82 0 2 s(x2)
5 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 s(x2)
6 6.61 0.699 0.245 0.110 0.975 7.38 1.62 1.63 4.13 0 3 s(x2)
7 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 s(x2)
8 7.09 0.184 0.160 0.681 0.861 5.58 1.10 1.38 3.11 0 4 s(x2)
9 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 s(x2)
10 7.88 0.960 0.0939 0.676 0.252 4.62 0.253 1.21 3.16 0 5 s(x2)
# i 590 more rows
# i 3 more variables: .term <chr>, .draw <int>, .value <dbl>
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