CVcov: Cross-Validation.

Description Usage Arguments Details Value Author(s) References See Also

Description

Cross-Validation to estimate regularization parameters for sparse inverse covariance estimation.

Usage

1
CVcov(x, maxlam, minlam, steps, pmiss = 0.01, do = 2, trace = TRUE)

Arguments

x

Data matrix.

maxlam

Maximum regularization parameter.

minlam

Minimum regularization parameter.

steps

Number of regularization parameters to test.

pmiss

Percentage missing in each fold.

do

Number of folds. Note that for medium or large size data matrices, often one fold is sufficient.

trace

Logical. Output the penalized log-likelihood and MSE for each step and fold.

Details

For internal use.

Value

cvmat

Matrix of cross-validation mean squared errors.

optlam

Optimal value of the regularization parameter as estimated by cross-validation.

lams

Values of the regularization parameters tested.

Author(s)

Genevera I. Allen

References

G. I. Allen and R. Tibshirani, "Transposable regularized covariance models with an application to missing data imputation", Annals of Applied Statistics, 4:2, 764-790, 2010.

See Also

covTranspose11, TransSphere


Tsphere documentation built on May 2, 2019, 3:32 p.m.

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