Support-vector regression for genomic prediction

Usage

morie_svm_genomic(x, y, markers, C = 1, epsilon = 0.1, gamma = "scale")

Arguments

x: Optional fixed-effect features.
y: Numeric response.
markers: (n x m) genotype matrix.
C: Cost (default 1).
epsilon: SVR tube width (default 0.1).
gamma: RBF kernel scale ("scale" = 1/(m * var(M)) or numeric).

Value

list(estimate, y_hat, alpha, support_indices, se, n, method).

References

Vapnik (1995); Montesinos Lopez Ch 7.

Examples

morie_svm_genomic(x = rnorm(50), y = rnorm(50), markers = matrix(sample(0:2, 200, TRUE), 50, 4))
#> $estimate
#> [1] -0.3258167
#> 
#> $y_hat
#>  [1] -0.533032672 -0.833434362 -0.474458929 -0.604967622  0.341024443
#>  [6] -0.931731859  0.045248597 -1.308026706 -0.445206475  0.032654375
#> [11] -0.904321455 -0.093415796 -0.235793901 -0.705610890 -0.285421827
#> [16] -1.078172396  0.087669894  0.021094569 -0.400587817 -0.091844403
#> [21]  0.600153544  0.557680934 -1.005242617 -0.209233380  0.426243624
#> [26] -0.377835491 -0.342837293  0.005975887 -0.979847865 -0.505123104
#> [31] -0.124348271 -0.321918362  0.053977550 -0.572986905 -0.522511139
#> [36]  0.251096451 -0.776735160 -0.231778140 -0.457467734  0.328513542
#> [41] -0.182164143 -0.375742600 -0.960560229  0.034480835 -0.708566734
#> [46] -0.615201046 -0.123656886 -0.083407787 -0.928703113  0.255244166
#> 
#> $alpha
#>  [1] -1.00000000  0.13375985  0.14696858  1.00000000 -1.00000000  0.30477753
#>  [7] -1.00000000 -1.00000000  0.57427278 -0.49070453  0.19583542 -1.00000000
#> [13]  0.02927450 -1.00000000 -1.00000000  0.44452536  1.00000000 -0.16073175
#> [19]  0.57662691  1.00000000  1.00000000 -0.58726197  0.25644096  1.00000000
#> [25] -0.65992002  1.00000000 -0.64650165  0.36990146  1.00000000 -0.22669314
#> [31]  1.00000000  0.05460851  1.00000000 -1.00000000  1.00000000 -1.00000000
#> [37]  1.00000000 -0.26295855 -0.20275276 -1.00000000 -0.38154092  1.00000000
#> [43] -0.11381790 -1.00000000 -0.35410868
#> 
#> $support_indices
#>  [1]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
#> [26] 27 28 29 31 32 33 34 36 37 38 39 40 41 42 43 45 47 48 49 50
#> 
#> $intercept
#> [1] -0.2434495
#> 
#> $se
#> [1] 0.604817
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "e1071 eps-SVR (RBF)"
#>