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

In penalizedclr: Integrative Penalized Conditional Logistic Regression

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
)

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.

Value

A list with a numeric vector Pistab giving selection probabilities for each penalized covariate, and a sequence lambda.seq used.

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

stable1 <- stable.clr(response = Y, penalized = X, stratum = stratum,
                         lambda.seq = lambda)

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