vboot.glm: Internal bootstraping validation logistic model

Description Usage Arguments

View source: R/parallel_vboot.R

Description

Validate logistic regression using bootstrap.

Usage

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## S3 method for class 'glm'
vboot(
  fit,
  x = NULL,
  y = NULL,
  s = NULL,
  gamma = NULL,
  nfolds = NULL,
  B = 200,
  cv_replicates = NULL,
  n_cores = max(1, parallel::detectCores() - 1)
)

Arguments

fit

Object from glm fit

x

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

y

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.

s

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

gamma

Value of "gamma" parameter selected for relaxed model

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

n_cores

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


Ancamar/bootValidation documentation built on April 26, 2020, 11:39 a.m.