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"))nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
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).
specify the number of folds for cross validation.
option to run CV in parallel. Defaults to cores = 1.
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:
optimal tuning parameter.
minimum average cross validation error for optimal parameters.
average cross validation error across all folds.
cross validation errors (negative validation likelihood).