phmm (version 0.7-5)

pseudoPoisPHMM: Pseudo poisson data for fitting PHMM via GLMM

Description

Function for generating a pseudo Poisson data set which can be used to fit a PHMM using GLMM software. This follows the mixed-model extension Whitehead (1980), who described how to fit Cox (fixed effects) models with GLM software.

Usage

pseudoPoisPHMM(x)

Arguments

x

an object of class phmm.

Value

A data.frame with columns:

References

Whitehead, J. (1980). Fitting Cox's Regression Model to Survival Data using GLIM. Journal of the Royal Statistical Society. Series C, Applied statistics, 29(3). 268-.

See Also

phmm, traceHat

Examples

## Not run: 
# 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$event <- event
# 
# fit.phmm <- phmm(Surv(time, event) ~ Z1 + Z2 + (-1 + Z1 + Z2 | cluster),
#    phmmd, Gbs = 100, Gbsvar = 1000, VARSTART = 1,
#    NINIT = 10, MAXSTEP = 100, CONVERG=90)
# 
# # Same data can be fit with lmer,
# # though the correlation structures are different.
# poisphmmd <- pseudoPoisPHMM(fit.phmm)
# 
# library(lme4)
# fit.lmer <- lmer(m~-1+as.factor(time)+z1+z2+
#   (-1+w1+w2|cluster)+offset(log(N)),
#   as.data.frame(as(poisphmmd, "matrix")), family=poisson)
# 
# fixef(fit.lmer)[c("z1","z2")]
# fit.phmm$coef
# 
# VarCorr(fit.lmer)$cluster
# fit.phmm$Sigma
# 
# logLik(fit.lmer)
# fit.phmm$loglik
# 
# traceHat(fit.phmm)
# ## End(Not run)