hit (version 0.3.1)

opt.penalty: Cross-validation of LASSO penenlty lambda

Description

Cross-validation of LASSO penenlty lambda.

Usage

opt.penalty(x, y, family, nfolds, lambda.opt, alpha, penalty.factor, n.samp2, mc.cores, ...)

Arguments

x

Design matrix, of dimension n x p.

y

Vector of quantitative response variable.

family

Distribution family of y.

nfolds

Number of folds (default is 10). See

lambda.opt

Criterion for optimum selection of cross-validated lasso. Either "lambda.1se" (default) or "lambda.min". See cv.glmnet for more details.

alpha

A single value n the range of 0 to 1 for the elastic net mixing parameter.

penalty.factor

See glmnet.

n.samp2

Number of individuals in samp2 which is the max. for non zero coefficients.

mc.cores

Number of cores for parallelising. Theoretical maximum is 'B'. For details see mclapply.

...

Additional agruments.