penalizedclr (version 2.0.0)

stable.clr: Stability selection based on penalized conditional logistic regression

Description

Performs stability selection for conditional logistic regression models with L1 and L2 penalty.

Usage

stable.clr(
  response,
  stratum,
  penalized,
  unpenalized = NULL,
  lambda.seq,
  alpha = 1,
  B = 100,
  parallel = TRUE,
  standardize = TRUE,
  event
)

Value

A list with a numeric vector Pistab

giving selection probabilities for each penalized covariate, and a sequence lambda.seq used.

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.seq

a sequence of non-negative value to be used as tuning parameter for L1

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.

parallel

Logical. Should the computation be parallelized?

standardize

Should the covariates be standardized, a logical value.

event

If response is a factor, the level that should be considered a success in the logistic regression.

See Also

stable.clr.g for stability selection in penalized conditional logistic regression with multiple penalties for block structured covariates.

Examples

 set.seed(123)

# simulate covariates (pure noise in two blocks of 20 and 80 variables)
X <- cbind(matrix(rnorm(4000, 0, 1), ncol = 20), matrix(rnorm(16000, 2, 0.6), ncol = 80))

# stratum membership
stratum <- sort(rep(1:100, 2))

# the response
Y <- rep(c(1, 0), 100)

# default L1 penalty
lambda <- find.default.lambda(response = Y,
                                   penalized = X,
                                   stratum = stratum)

# perform stability selection
# \donttest{
stable1 <- stable.clr(response = Y, penalized = X, stratum = stratum,
                         lambda.seq = lambda)# }