K-fold cross-validation for genomic-prediction accuracy

Usage

morie_genomic_cross_validation(x, y, K = 5, lam = 1, seed = 0)

Arguments

x: (n x p) predictor matrix.
y: Numeric response.
K: Number of folds.
lam: Ridge penalty within each fold.
seed: Seed.

Value

list(estimate, r_per_fold, y_hat, mse, mspe, slope, n, K, method).

References

Montesinos Lopez Ch 2.

Examples

morie_genomic_cross_validation(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] -0.2193689
#> 
#> $r_per_fold
#> [1]  0.632115047 -0.477119885  0.576136993  0.306374237 -0.002116466
#> 
#> $y_hat
#>  [1]  1.809963e-01  3.890422e-02  6.171128e-02  1.439004e-01 -2.263094e-02
#>  [6] -2.027688e-01 -1.464229e-01 -8.496834e-03  1.373471e-01  9.340583e-01
#> [11]  8.021333e-02 -1.344827e-01  7.768737e-03 -8.752205e-02  4.035442e-02
#> [16]  3.237559e-03  6.083021e-02 -1.954076e-01  6.778413e-02 -1.748981e-01
#> [21]  7.381025e-05  7.326695e-02 -3.467671e-02  4.050604e-01 -6.610791e-02
#> [26]  7.035877e-02  3.922114e-02 -8.912614e-02 -1.792353e-01  3.304059e-02
#> [31]  4.233557e-02 -1.406893e-01 -1.235106e-01  2.665210e-02  3.794647e-01
#> [36]  1.674878e-01  9.996223e-02 -1.229153e-01  1.779976e-01  4.692163e-02
#> [41]  1.179019e-01  3.245956e-01 -1.265635e-01 -7.386285e-02 -2.463307e-01
#> [46] -1.022742e-01 -8.004188e-03  1.257318e-01  7.486107e-02 -2.985408e-04
#> 
#> $mse
#> [1] 0.8262322
#> 
#> $mspe
#> [1] 0.8262322
#> 
#> $slope
#> [1] -0.04896032
#> 
#> $n
#> [1] 50
#> 
#> $K
#> [1] 5
#> 
#> $method
#> [1] "K-fold cross-validation (ridge)"
#>