CVP: Parallel Cross Validation

In CVglasso: Lasso Penalized Precision Matrix Estimation

Description

Parallel implementation of cross validation.

Usage

CVP(X = NULL, lam = 10^seq(-2, 2, 0.2), diagonal = FALSE, tol = 1e-04,
  maxit = 10000, adjmaxit = NULL, K = 5, crit.cv = c("loglik", "AIC",
  "BIC"), start = c("warm", "cold"), cores = 1, trace = c("progress",
  "print", "none"), ...)

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 elastic net penalty. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values 10^seq(-2, 2, 0.2).
diagonal	option to penalize the diagonal elements of the estimated precision matrix (Ω). Defaults to FALSE.
tol	convergence tolerance. Iterations will stop when the average absolute difference in parameter estimates in less than tol times multiple. Defaults to 1e-4.
maxit	maximum number of iterations. Defaults to 1e4.
adjmaxit	adjusted maximum number of iterations. During cross validation this option allows the user to adjust the maximum number of iterations after the first lam tuning parameter has converged. This option is intended to be paired with warm starts and allows for 'one-step' estimators. Defaults to NULL.
K	specify the number of folds for cross validation.
crit.cv	cross validation criterion (loglik, AIC, or BIC). Defaults to loglik.
start	specify warm or cold start for cross validation. Default is warm.
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.
...	additional arguments to pass to glasso.

Value

returns list of returns which includes:

lam	optimal tuning parameter.
min.error	minimum average cross validation error (cv.crit) for optimal parameters.
avg.error	average cross validation error (cv.crit) across all folds.
cv.error	cross validation errors (cv.crit).

CVglasso documentation built on May 2, 2019, 4:01 a.m.