MCP.Xie: A wrapper for divide-and-conquer MCP Cox model proposed by...

In michaelyanwang/divideconquer: Divide-and-conquer adaptive lasso using least square approximation

Description

MCP.Xie fits Cox model with an MCP penalty. The best lambda (penalizing factor) is chosen by BIC.

Usage

MCP.Xie(dat.list, K, lambda.grid, mvpct = 0.5)

Arguments

dat.list	a list of matrices. Each element of the list is a sub-dataset. In each sub-dataset, for non-survival outcome, first column = response, rest = design matrix. In each sub-dataset, for continuous-time survival outcome, first column = time, second column = delta (0/1), rest = design matrix.
K	Number of sub-datasets in dat.list
lambda.grid	the grid of lambda to put into glmnet
mvpct	majority voting percentage used for Chen and Xie (2014)
BIC.factor	factor in modified BIC, BIC = -2 loglikelihood + df * N^BIC.factor

Value

a list containing the vector of estimates.

Author(s)

Yan Wang, Tianxi Cai

References

Chen, Xueying, and Min-ge Xie. "A split-and-conquer approach for analysis of extraordinarily large data." Statistica Sinica (2014): 1655-1684.

Examples

MCP.Xie(dat.list,K,BIC.factor=0.1,lambda.grid,mvpct = 0.5)

michaelyanwang/divideconquer documentation built on Aug. 16, 2019, 10:11 a.m.