CVP_RIDGE: Parallel Ridge CV (uses CVP_RIDGEc)

In 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).

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