phmm-package {phmm} | R Documentation |
Fits proportional hazards model incorporating random effects. The function implements an EM agorithm using Markov Chain Monte Carlo at the E-step as described in Vaida and Xu (2000).
Package: | phmm |
Version: | 0.2 |
Date: | 2008-01-15 |
Depends: | survival |
Suggests: | lme4 |
License: | GPL2 |
Packaged: | Fri Jul 11 10:33:57 2008; mdonohue |
Built: | R 2.8.0; universal-apple-darwin8.11.1; 2008-11-29 12:05:00; unix |
Index:
AIC.phmm Akaike Information Criterion for PHMM cAIC Conditional Akaike Information Criterion for PHMM e1582 Eastern Cooperative Oncology Group (EST 1582) linear.predictors PHMM Design loglik.cond PHMM conditional log-likelihood phmm Proportional Hazards Model with Mixed Effects phmm-package Proportional Hazards Model with Mixed Effects phmm.cond.loglik PHMM conditional log-likelihood phmm.design PHMM Design pseudoPoisPHMM Pseudo poisson data for fitting PHMM via GLMM traceHat Trace of the "hat" matrix from PHMM-MCEM fit
Ronghui Xu, Michael Donohue
Maintainer: Michael Donohue mdonohue@ucsd.edu
Vaida, F. and Xu, R. "Proportional hazards model with random effects", Statistics in Medicine, 19:3309-3324, 2000.
N <- 100 B <- 100 n <- 50 nclust <- 5 clusters <- rep(1:nclust,each=n/nclust) beta0 <- c(1,2) set.seed(13) #generate phmm data set Z <- cbind(Z1=sample(0:1,n,replace=TRUE), Z2=sample(0:1,n,replace=TRUE), Z3=sample(0:1,n,replace=TRUE)) b <- cbind(rep(rnorm(nclust),each=n/nclust),rep(rnorm(nclust),each=n/nclust)) Wb <- matrix(0,n,2) for( j in 1:2) Wb[,j] <- Z[,j]*b[,j] Wb <- apply(Wb,1,sum) T <- -log(runif(n,0,1))*exp(-Z[,c('Z1','Z2')]%*%beta0-Wb) C <- runif(n,0,1) time <- ifelse(T<C,T,C) event <- ifelse(T<=C,1,0) mean(event) phmmdata <- data.frame(Z) phmmdata$cluster <- clusters phmmdata$time <- time phmmdata$event <- event fit.phmm <- phmm(Surv(time, event)~Z1+Z2+cluster(cluster), ~-1+Z1+Z2, phmmdata, Gbs = 100, Gbsvar = 1000, VARSTART = 1, NINIT = 10, MAXSTEP = 100, CONVERG=90) summary(fit.phmm)