CVP_ADMM: Parallel CV (uses CV_ADMMc)

Description Usage Arguments Value

Description

Parallel implementation of cross validation.

Usage

1
2
3
4
5
6
CVP_ADMM(X = NULL, lam = 10^seq(-2, 2, 0.2), alpha = seq(0, 1, 0.2),
  diagonal = FALSE, rho = 2, mu = 10, tau.inc = 2, tau.dec = 2,
  crit = c("ADMM", "loglik"), tol.abs = 1e-04, tol.rel = 1e-04,
  maxit = 1000, adjmaxit = NULL, K = 5, crit.cv = c("loglik",
  "penloglik", "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).

alpha

elastic net mixing parameter contained in [0, 1]. 0 = ridge, 1 = lasso. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values seq(-1, 1, 0.2).

diagonal

option to penalize the diagonal elements of the estimated precision matrix (Ω). Defaults to FALSE.

rho

initial step size for ADMM algorithm.

mu

factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size rho after each iteration.

tau.inc

factor in which to increase step size rho

tau.dec

factor in which to decrease step size rho

crit

criterion for convergence (ADMM or loglik). If crit = loglik then iterations will stop when the relative change in log-likelihood is less than tol.abs. Default is ADMM and follows the procedure outlined in Boyd, et al.

tol.abs

absolute convergence tolerance. Defaults to 1e-4.

tol.rel

relative convergence tolerance. Defaults to 1e-4.

maxit

maximum number of iterations. Defaults to 1e3.

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 (for each alpha). 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, penloglik, 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.

Value

returns list of returns which includes:

lam

optimal tuning parameter.

alpha

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).


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