TpowerPCACV: Sparsity parameter selection for the Yuan and Zhang TPower...

In EESPCA: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

Sparsity parameter selection for the Yuan and Zhang TPower method using cross-validation.

Description

Sparsity parameter selection for PCA-based TPower using the cross-validation approach of Witten et al. as implemented by the SPC.cv method in the PMA package.

Usage

    tpowerPCACV(X, k.values, nfolds=5)

Arguments

X	n-by-p data matrix being evaluated via PCA.
k.values	Set of truncation parameter values to evaluate via cross-validation. Values must be between 1 and p.
nfolds	Number of folds for cross-validation

Value

k value that generated the smallest cross-validation error.

References

• Yuan, X.-T. and Zhang, T. (2013). Truncated power method for sparse eigenvalue problems. J. Mach. Learn. Res., 14(1), 899-925.

• Witten, D. M., Tibshirani, R., and Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10(3), 515-534.

See Also

tpower,PMA{SPC.cv}

Examples

    set.seed(1)
    # Simulate 10x5 MVN data matrix
    X=matrix(rnorm(50), nrow=10)
    # Generate range of k values to evaluate
    k.values = 1:5
    # Use 5-fold cross-validation to estimate optimal k value
    tpowerPCACV(X=X, k.values=k.values)

EESPCA documentation built on June 16, 2022, 1:07 a.m.