fastCV2: Fast cross-validation per data block

In multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression

Fast cross-validation per data block

Description

Fast cross-validation for high-dimensional data. Finds optimal penalties separately per data block. Useful for initialization.

Usage

fastCV2(XXblocks, Y, X1 = NULL, kfold = 10, intercept =
ifelse(is(Y, "Surv"), FALSE, TRUE), parallel = FALSE, fixedfolds = TRUE,
model = NULL, eps = 1e-10, reltol = 0.5, lambdamax= 10^6, traceCV=TRUE)

Arguments

XXblocks	List of data frames or matrices, representing b=1,...,B data blocks of dimensions n x p_b.
Y	Response vector: numeric, binary, factor or survival.
X1	Matrix. Dimension n x p_0, p_0 < n, representing unpenalized covariates.
kfold	Integer. Desired fold.
intercept	Boolean. Should an intercept be included?
parallel	Boolean. Should computation be done in parallel? If TRUE, requires to run setupParallel first.
fixedfolds	Boolean. Should fixed splits be used for reproducibility?
model	Character. Any of c("linear", "logistic", "cox"). Is inferred from Y when NULL.
eps	Scalar. Numerical bound for IWLS convergence.
reltol	Scalar. Relative tolerance for optimization method.
lambdamax	Numeric. Upperbound for lambda.
traceCV	Boolean. Should the CV results be traced and printed?

Details

This function is basically a wrapper for applying optLambdas per data block separately using Brent optimization.

Value

Numerical vector containing penalties optimized separately per data block. Useful for initialization.

See Also

optLambdas, optLambdasWrap which optimize the penalties jointly. A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

Examples

data(dataXXmirmeth)
resp <- dataXXmirmeth[[1]]
XXmirmeth <- dataXXmirmeth[[2]]

cvperblock2 <- fastCV2(XXblocks=XXmirmeth,Y=resp,kfold=10,fixedfolds = TRUE)
lambdas <- cvperblock2$lambdas

multiridge documentation built on June 13, 2022, 5:07 p.m.