This function does k-fold cross-validation for the network-based logistic regression and returns a pair of lambda1 and lambda2.
CV.NetLogistic(X, Y, lamb.1 = NULL, lamb.2 = 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 lambda1 values, which serves as a tuning parameter to impose sparsity. If it is left as NULL, a default sequence will be used.
a user-supplied sequence of lambda2 values, which serves as a tuning parameter to control the smoothness among coefficient profiles. If it is left as NULL, a default sequence, (0.1, 1, 10), will be used.
the regularization parameter in MCP; default is 5.
by default, the program uses 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 to be -1, the program will use zeroes as initial coefficients.
the number of folds for cross-validation; default is 5.
output progress to the console.
a list with components:
the optimal pair of lambda1 and lambda2.
the misclassification rate of the optimal pair of lambda1 and lambda2.
a matrix of the misclassification rates for all pairs of lambdas tested.
Ren, J., He, T., Li, Y., Liu, S., Du, Y., Jiang, Y., Wu, C. (2017). Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes. BMC Genetics, 18(1):44.