This function does k-fold cross-validation for the MCP logistic regression and returns the optimal value of lambda.
CV.McpLogistic(X, Y, lambda = NULL, r = 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 regularization parameter in MCP.
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 is 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.
zhang, CH. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2):894-942.