CV_RIDGEc: CV ridge penalized precision matrix estimation (c++)

In ADMMsigma: Penalized Precision Matrix Estimation via ADMM

Description

Cross validation function for RIDGEsigma.

Usage

CV_RIDGEc(X, S, lam, path = FALSE, K = 3L, trace = "none")

Arguments

X	option to provide a nxp matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
S	option to provide a pxp sample covariance matrix (denominator n). If argument is NULL and X is provided instead then S will be computed automatically.
lam	positive tuning parameters for ridge penalty. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values 10^seq(-5, 5, 0.5).
path	option to return the regularization path. This option should be used with extreme care if the dimension is large. If set to TRUE, cores will be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE.
K	specify the number of folds for cross validation.
trace	option to display progress of CV. Choose one of progress to print a progress bar, print to print completed tuning parameters, or none.

Value

list of returns includes:

lam	optimal tuning parameter.
path	array containing the solution path. Solutions are ordered dense to sparse.
min.error	minimum average cross validation error for optimal parameters.
avg.error	average cross validation error across all folds.
cv.error	cross validation errors (negative validation likelihood).

ADMMsigma documentation built on May 2, 2019, 6:23 a.m.