This function does k-fold cross-validation for the Elastic-Net logistic regression and returns the optimal value of lambda.
CV.ElasLogistic(X, Y, lambda = NULL, alpha = 0.5, alpha.i = 1,
folds = 5, verbo = FALSE)a matrix of predictors.
a vector of the binary response.
a user-supplied sequence of lambda values, which serves as a tuning parameter to impose sparsity. If it is left as NULL, a default sequence will be used.
the Elastic-Net mixing parameter, with \(0 \le \alpha \le 1\). alpha=1 corresponds to the lasso penalty, and alpha=0 corresponds to the ridge penalty.
by default, the program uses the lasso penalty 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 to be -1, the program will use zeroes as initial coefficients.
the number of folds for cross-validation.
output progress to the console.
a list with components:
the optimal lambda.
the misclassification rate of the optimal lambda.
a matrix of the misclassification rates for all the values of lambda tested.
Zou H, Hastie T. (2005). Regularization and variable selection via the elastic net. J.R. Statist.Soc.B, 67(2):301<U+2013>20.