loglik.pchsurv: Likelihood function of a piecewise constant hazard model

In obouaziz/pchsurv: Piecewise constant estimation of the hazard function for time-to-event data

Description

Given time to event data, a set of cuts and a log-hazard value, compute the maximum likelihood function

Usage

loglik.pchsurv(time, status, cuts, a, weights = NULL)

Arguments

time	the observed time data.
status	status of the time data. TRUE for true time and FALSE for censored time.
cuts	a sequence of cuts. Default to NULL which corresponds to the exponential model.
a	sequence of log-hazard values. Should be of length equal to length(cuts)+1.
weights	an optional weight sequence. Default to NULL.

Examples

n=400
cuts=c(20,40,50,70)
alpha=c(0,0.05,0.1,0.2,0.4)/10
time=rsurv(n,cuts,alpha) #generate true data from the pch model
censoring=runif(n,min=70,max=90)
time=pmin(time,censoring) #observed times
delta=time<censoring #gives 62% of observed data on average
result=mlepchsurv(time,delta,cuts)
loglik.pchsurv(time,delta,cuts,result$a)

obouaziz/pchsurv documentation built on Sept. 7, 2020, 11:03 a.m.