getfinalSCADcoefCOX {SIS}R Documentation

SCAD regularized loglikelihood for Cox proportional hazards regression models

Description

This function gets the final regression coefficients for the SCAD regularized loglikelihood for Cox proportional hazards regression models after applying (I)SIS

Usage

getfinalSCADcoefCOX(x, time, status, method = "efron", pickind, 
folds = NULL, eps0 = 1e-3, tune.method = "AIC", inittype = "NoPen", 
detailed = FALSE)

Arguments

x an (n * p) matrix of features.
time an (n) vector of the follow up time for right censored data.
status an (n) vector of the status indicator, normally 0=alive, 1=dead.
method indicates how to handle observations that have tied (i.e., identical) survival times. The default "efron" method is generally preferred to the once-popular "breslow" method.
pickind predictor indice selected by (I)SIS.
folds fold information for cross validation.
eps0 an effecitve zero.
tune.method method for tuning regularization parameter.
inittype inittype specifies the type of initial solution for the one-step SCAD. It can be either NoPen or L1.
detailed indicates whether detailed information will be provided. Default is FALSE.

Value

An initial solution vector wt.initsoln and final solution (p) vector SCADcoef.

Author(s)

Jianqing Fan, Yang Feng, Richard Samworth, and Yichao Wu

References

Jianqing Fan and Runze Li (2002) Variable Selection for Cox's Proportional Hazards Model and Frailty Model. The Annals of Statistics, 30, 74-99.

Hui Zou and Runze Li (2008) One-step Sparse Estimates in Nonconcave Penalized Likelihood Models (with discussion). The Annals of Statistics, 36, 1509-1533

See Also

scadcox, fullscadcox

Examples

set.seed(0)
n=150
p=200
truerho=0.5
beta <- c(4,4,4,-6*sqrt(2),4/3, rep(0,p-5))

corrmat=diag(rep(1-truerho, p))+matrix(truerho, p, p)
corrmat[,4]=sqrt(truerho)
corrmat[4, ]=sqrt(truerho)
corrmat[4,4]=1
corrmat[,5]=0
corrmat[5,]=0
corrmat[5,5]=1
cholmat=chol(corrmat)

x=matrix(rnorm(p*n, mean=0, sd=1), n, p)
x=x%*%cholmat

myrates <- exp(x%*%beta)

ytrue <- rexp(n, rate = myrates) 
cen <- rexp(n, rate = 0.1 )
time <- pmin(ytrue, cen)
status <- as.numeric(ytrue <= cen)

SIScoef <- getfinalSCADcoefCOX(x = x, time = time, status = status, 
            pickind = 1:5)

[Package SIS version 0.2 Index]