Internal function used by stable.clr and stable.clr.g.
subsample.clr(
response,
stratum,
penalized,
unpenalized = NULL,
lambda,
alpha = 1,
B = 100,
matB = NULL,
return.matB = FALSE,
parallel = TRUE,
standardize = TRUE
)If return.matB is TRUE, a list with two elements, a numeric vector Pistab,
giving selection probabilities for each covariate and a matrix matB;
otheriwise only Pistab.
The response variable, either a 0/1 vector or a factor with two levels.
A numeric vector with stratum membership of each observation.
A matrix of penalized covariates.
A matrix of additional unpenalized covariates.
The tuning parameter for L1. Either a single non-negative number, or a numeric vector of the length equal to the number of blocks. See p below.
The elastic net mixing parameter, a number between 0 and 1. alpha=0 would give pure ridge; alpha=1 gives lasso. Pure ridge penalty is never obtained in this implementation since alpha must be positive.
A single positive number for the number of subsamples.
A 2B x ceiling(unique(stratum)/2) matrix with index set of selected strata in each of 2B subsamples
Logical. Should the matrix matB be returned?
Logical. Should the computation be parallelized?
Should the covariates be standardized, a logical value.