regnet (version 0.2.0)

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

Description

This function makes predictions for Elastic-Net logistic regression 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 that imposes sparsity.

alpha

the Elastic-Net 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 alpha.i is assigned as -1, the program will use zeroes as initial coefficients.

folds

the number of folds for cross-validation.

Value

the estimated coefficients vector.

References

Zou H, Hastie T. (2005). Regularization and variable selection via the elastic net. J.R. Statist.Soc.B, 67(2):301<U+2013>20.

See Also

CV.ElasLogistic

Examples

# NOT RUN {
b = ElasLogistic(regnet$X, regnet$Y, 0.04)
inds = which(regnet$beta != 0)
sel = which(b != 0)
tp = length(intersect(inds, sel))
fp = length(sel) - tp
list(tp=tp, fp=fp)
# }