vboot.coxnet: Internal bootstraping validation cox glmnet model

In BootValidation: Adjusting for Optimism in 'glmnet' Regression using Bootstrapping

Description

Validate glmnet cox regression using bootstrap.

Usage

## S3 method for class 'coxnet'
vboot(fit, x, y, s, nfolds = 5, B = 200,
  cv_replicates = 100, lambda = TRUE, n_cores = max(1,
  parallel::detectCores() - 1))

Arguments

fit	Object from glmnet fit.
x	A matrix of the predictors, each row is an observation vector.
y	Should be a two-column matrix with columns named 'time' and 'status' as in 'glmnet'.
s	Value of the penalty parameter "lambda" selected from the original 'cv.glmnet'.
nfolds	Number of folds for cross validation as in 'cv.glmnet'.
B	Number of bootsrap samples.
cv_replicates	Number of replicates for the cross-validation step.
lambda	By default, the validation is adjusted using 'lambda.1se' which has error within 1 standard error of the best model. If 'FALSE' the 'lambda.min' referered to the lowest CV error will be used.
n_cores	number of cores to use in parallel. Default detectCores()-1.

BootValidation documentation built on May 1, 2019, 8:24 p.m.