MSPE score of ridge regression

The approximation of the expected mean squared prediction error of ridge regression, \(1 + tr[X_0 A A' X_0']/n_0 + tr[(X_0 - X_0 A X)'(X_0 - X_0 A X)]\) with \(A = X'(XX' + \lambda I)^{-1}\). Smaller is better. The default penalty is \(\lambda = 1\).

Usage

mspe_score(X, X0, lambda = NULL)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

lambda

A positive number, the ridge penalty, or NULL (default) for the default of the criterion.

Value

A number.

Details

See criteria for the list of criteria and the package website for one page per criterion.

References

Chen, S.-P., Sung, W.-H., & Liao, C.-T. (2024). Constructing training sets for genomic selection to identify superior genotypes in candidate populations. Theoretical and Applied Genetics, 137, 270. <doi:10.1007/s00122-024-04766-y>

Author

Jen-Hsiang Ou

Examples

data(geno)
mspe_score(geno[1:50, ], geno[51:100, ])
#> [1] 636411