subsample.clr: Stability selection for penalized conditional logistic...

In penalizedclr: Integrative Penalized Conditional Logistic Regression

Stability selection for penalized conditional logistic regression

Description

Internal function used by stable.clr and stable.clr.g.

Usage

subsample.clr(
  response,
  stratum,
  penalized,
  unpenalized = NULL,
  lambda,
  alpha = 1,
  B = 100,
  matB = NULL,
  return.matB = FALSE,
  parallel = TRUE,
  standardize = TRUE
)

Arguments

response	The response variable, either a 0/1 vector or a factor with two levels.
stratum	A numeric vector with stratum membership of each observation.
penalized	A matrix of penalized covariates.
unpenalized	A matrix of additional unpenalized covariates.
lambda	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.
alpha	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.
B	A single positive number for the number of subsamples.
matB	A 2B x ceiling(unique(stratum)/2) matrix with index set of selected strata in each of 2B subsamples
return.matB	Logical. Should the matrix matB be returned?
parallel	Logical. Should the computation be parallelized?
standardize	Should the covariates be standardized, a logical value.

Value

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.

penalizedclr documentation built on July 26, 2023, 5:18 p.m.