Parallel Ridge CV (uses CVP_RIDGEc)
Parallel implementation of cross validation for RIDGEsigma.
CVP_RIDGE(X = NULL, lam = 10^seq(-2, 2, 0.1), K = 5, cores = 1, trace = c("none", "progress", "print"))
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.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).