Description Details Author(s) References Examples
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:
1 2 3 4 5 6 7 8 | 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
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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.
Donohue, MC, Overholser, R, Xu, R, and Vaida, F (January 01, 2011). Conditional Akaike information under generalized linear and proportional hazards mixed models. Biometrika, 98, 3, 685-700.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | n <- 50 # total sample size
nclust <- 5 # number of clusters
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)
phmmd <- data.frame(Z)
phmmd$cluster <- clusters
phmmd$time <- time
phmmd$event <- event
fit.phmm <- phmm(Surv(time, event) ~ Z1 + Z2 + (-1 + Z1 + Z2 | cluster),
phmmd, Gbs = 100, Gbsvar = 1000, VARSTART = 1,
NINIT = 10, MAXSTEP = 100, CONVERG=90)
summary(fit.phmm)
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