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Tsphere (version 1.0)

CVcov: Cross-Validation.

Description

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

Usage

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.

Value

  • cvmatMatrix of cross-validation mean squared errors.
  • optlamOptimal value of the regularization parameter as estimated by cross-validation.
  • lamsValues of the regularization parameters tested.

Details

For internal use.

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