wtlassocox: Weighted L1 regularized loglikelihood for Cox proportional hazards regression model

Description

This functions solves weighted L1 regularized loglikelihood for Cox proportional hazards regression model.

Usage

wtlassocox(x, time, status, method="efron", lassoweight=NULL, initsoln=NULL,  
weight = NULL, lambda2=0, function.precision=1e-10)

Arguments

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.

lassoweight

an optional (p) vector of weights specifying the weighted L1 penalty.

initsoln

an optional (p) vector of initial solution.

weight

an optional (n) vector of weights to be used in the fitting process.

lambda2

regularization parameter for the L2 norm of the coefficients. Default is 0.

function.precision

function.precision parameter used in the internal solver. Default is 1e-10.

Value

An object is returned with
lambda2$\lambda_2$ used.
xnamescolumn names of x.
weightan optional (n) vector of weights to be used in the fitting process.
lassoweighta (p) vector of weights specifying the weighted L1 penalty.
initsolna (p) vector of initial solution.
wa (p) vector of weight L1 solution.

Details

This function solves weighted L1 regularized loglikelihood for Cox proportional hazards regression model. It is based on the source code of R package glmpath.

Examples

Run this code

set.seed(0)
n=400
p=1000
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)



weights <- rep(0.01*n, 100)
w1 <- wtlassocox(x[,1:100],time,status,lassoweight=weights)$w
w2 <- coxph(Surv(time,status)~x[,1:100])$coef