CVP_RIDGE: Parallel Ridge CV (uses CVP_RIDGEc)

In MGallow/ADMMsigma: Penalized Precision Matrix Estimation via ADMM

Description

Parallel implementation of cross validation for RIDGEsigma.

Usage

CVP_RIDGE(X = NULL, lam = 10^seq(-2, 2, 0.1), K = 5, cores = 1,
  trace = c("none", "progress", "print"))

Arguments

X	nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
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(-2, 2, 0.1).
K	specify the number of folds for cross validation.
cores	option to run CV in parallel. Defaults to cores = 1.
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

returns list of returns which includes:

lam	optimal tuning parameter.
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).

MGallow/ADMMsigma documentation built on May 15, 2019, 3:23 p.m.