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#' Proportional Hazards with Mixed Model (PHMM)
#'
#' 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).
#'
#' \tabular{ll}{ Package: \tab phmm\cr Version: \tab 0.2\cr Date: \tab
#' 2008-01-15\cr Depends: \tab survival\cr Suggests: \tab lme4\cr License: \tab
#' GPL2\cr Packaged: \tab Fri Jul 11 10:33:57 2008; mdonohue\cr Built: \tab R
#' 2.8.0; universal-apple-darwin8.11.1; 2008-11-29 12:05:00; unix\cr }
#'
#' Index: \preformatted{ 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 }
#'
#' @name phmm-package
#' @docType package
#' @author Ronghui Xu, Michael Donohue
#'
#' Maintainer: Michael Donohue \email{mdonohue@@ucsd.edu}
#' @references Vaida, F. and Xu, R. "Proportional hazards model with random
#' effects", \emph{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. \emph{Biometrika}, 98, 3, 685-700.
#' @keywords package
#' @examples
#' \donttest{
#' 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)
#' }
#' @useDynLib phmm
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