getfinalSCADcoefCOX {SIS} | R Documentation |
This function gets the final regression coefficients for the SCAD regularized loglikelihood for Cox proportional hazards regression models after applying (I)SIS
getfinalSCADcoefCOX(x, time, status, method = "efron", pickind, folds = NULL, eps0 = 1e-3, tune.method = "AIC", inittype = "NoPen", detailed = FALSE)
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 .
|
An initial solution vector wt.initsoln
and final solution (p) vector SCADcoef
.
Jianqing Fan, Yang Feng, Richard Samworth, and Yichao Wu
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
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)