regnet (version 0.1.1)

ElasLogistic: Elastic-net logistic regression for a given lambda.

Description

This function makes predictions for elastic-net logistic for a given value of lambda. Typical usage is to have the CV.ElasLogistic function compute the optimal lambda, then provide it to the ElasLogistic function.

Usage

ElasLogistic(X, Y, lambda, alpha = 0.5, alpha.i = 1, folds = 5)

Arguments

X

a matrix of predictors.

Y

a vector of the binary response.

lambda

the tuning parameter lambda imposes sparsity.

alpha

the elasticnet mixing parameter, with \(0 \le \alpha \le 1\). alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.

alpha.i

by default, the program use the lasso for choosing initial values of the coefficient vector. alpha.i is the elastic-net mixing parameter, with \(0 \le alpha.i \le 1\). alpha.i=1 is the lasso penalty, and alpha.i=0 the ridge penalty. If assign alpha.i to be -1, program will use zero as initial coefficients.

folds

the number of folds for cross-validation.

Value

the estimated coefficients vector.

See Also

CV.ElasLogistic

Examples

# NOT RUN {
b = ElasLogistic(regnet$X, regnet$Y, 0.04)
regnet$beta  # the true coefficient
# }