traceHat: Trace of the "hat" matrix from PHMM-MCEM fit

Description

Compute trace of the ``hat'' matrix from PHMM-MCEM fit using a direct approximation method (Donohue, et al, submitted), an approximation via hierarchical likelihoods (Ha et al, 2007), or an approximation via a generalized linear mixed-effects model (GLMM) (Donohue, et al, submitted).

Usage

traceHat(x, method = "direct")

Arguments

an object of class phmm,

method

acceptable values are "direct", "pseudoPois", or "HaLee",

Value

The trace of the "hat" matrix which can be used as a measure of complexity of the model.

References

Breslow, NE, Clayton, DG. (1993). Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association, Vol. 88, No. 421, pp. 9-25.

Donohue, M, Xu, R, Vaida, F, Haut R. Model Selection for Clustered Data: Conditional Akaike Information under GLMM and PHMM. Submitted.

Ha, ID, Lee, Y, MacKenzie, G. (2007). Model Selection for multi-component frailty models. Statistics in Medicine, Vol. 26, pp. 4790-4807.

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-.

Examples

Run this code

## 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)