vboot.lm

A matrix of the predictors, each row is an observation vector.

A vector of response variable. It should be quantitative for lineal regression, a factor with two levels for logistic regression or a two-column matrix with columns named 'time' and 'status' for cox regression.

Value of the penalty parameter "lambda" selected from the original 'cv.glmnet'.

Number of folds for cross validation as in 'cv.glmnet'.

nfolds

Number of replicates for the cross-validation step

cv_replicates

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.

lambda

number of cores to use in parallel. Default detectCores()-1

n_cores

Validate linear regression using bootstrap.

Main objective of a predictive model is to provide accurated predictions of a new observations.
Unfortunately we don't know how well the model performs. In addition, at the current era of omic
data where p >> n, is not reasonable applying internal validation using data-splitting. Under this
background a good method to assessing model performance is applying internal bootstrap validation
(Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation
for the linear, logistic, multinomial and cox 'glmnet' models as well as lm and glm models.

vboot.lm: Internal bootstraping validation linear model

Description

Usage

Arguments