regnet (version 0.1.1)

NetLogistic: Network-based logistic regression for given lambda1 and lambda2.

Description

This function makes predictions for network-based logistic for a given pair of lambda1 and lambda2. Typical usage is to have the CV.NetLogistic function compute the optimal lambdas, then provide them to NetLogistic function.

Usage

NetLogistic(X, Y, lamb.1, lamb.2, alpha.i = 1, r = 5, folds = 5)

Arguments

X

a matrix of predictors.

Y

a vector of the binary response.

lamb.1

the tuning parameter lambda1 imposes sparsity.

lamb.2

the tuning parameter lambda2 controls the smoothness among coefficient profiles.

alpha.i

by default, the program use the elastic-net 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.

r

the regularization parameter in MCP.

folds

the number of folds for cross-validation.

Value

the estimated coefficients vector.

References

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 <U+2013> control study of Type 2 diabetes. BMC Genetics.

See Also

CV.NetLogistic

Examples

# NOT RUN {
b = NetLogistic(regnet$X, regnet$Y, 0.05, 1)
regnet$beta
# }