View source: R/parallel_vboot.R
Validate glmnet logistic regression using bootstrap.
1 2 3 4 | ## S3 method for class 'multnet'
vboot(fit, x, y, s, nfolds = 5, B = 200,
cv_replicates = 100, lambda = TRUE, n_cores = max(1,
parallel::detectCores() - 1))
|
fit |
Object from glmnet fit. |
x |
A matrix of the predictors, each row is an observation vector. |
y |
A vector of response variable. Should be a factor with two levels. |
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 in 'cv.glmnet'. |
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. |
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