Description Usage Arguments Details Value Author(s) References See Also
Cross-Validation to estimate regularization parameters for sparse inverse covariance estimation.
1 |
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. |
For internal use.
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. |
Genevera I. Allen
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.