lmec {lmec}R Documentation

Linear Mixed-Effects Models with Censored Responses

Description

This generic function fits a linear mixed-effects model in the formulation described in Laird and Ware (1982) but allowing for censored normal responses. In this version, the with-in group errors are assumed independent and identically distributed.

Usage

lmec(yL, cens, X, Z, cluster, maxstep = 200, varstruct = "unstructured", init, method = "ML", epsstop = 0.001, abspmv = 0.001, mcmc0 = 100, sdl = 0.1, iter2 = 15, trs = 5, pls = 5, mcmcmax = 1000)

Arguments

yL Observed left-censored response vector
cens Censoring indicator: if yL>ytrue, then cens=1
X Design matrix for the fixed-effects model, it needs to include a column of 1's if the intercept is present
Z If the design matrix for the random-effects is diag(Z1, Z2, ..., Zm), then Z=(Z1',Z2', ..., Zm')'
cluster Cluster indicator taking values between 1 and m
maxstep The maximum number of EM iterations
varstruct Variance structure for random effects, current options are unstructured and diagonal.
init Intial estimated parameters (it is optional), it is a list with components beta, bi, sigma and Delta.
method Options are ML, REML and MLmcmc
epsstop The threshold for the difference between two consecutive likelihood values in EM sequence
abspmv Absolute error tolerance for pmvnorm() function
mcmc0 The burn-in MCMC sample size for E-step of EM
sdl The target standard deviation for the log-likelihood
iter2 Number of steps in stage 2 for evaluating standard deviation of log-likelihhood
trs Number of increase in sample size during transition face
pls Number of steps in plateau face of MCEM
mcmcmax Maximum MCEM sample size

Value

beta Estimated fixed effects
bi Estimated random effects
sigma Residual standard deviation
Psi Variance matrix of random effects
Delta Matrix such that Delta'*Delta=sigma2*solve(Psi)
loglik Maximum log-likelihood value (or surrogate objective function)
varFix Variance matrix for fixed effects
method Options are ML, REML and MLmcmc
varstruct Variance structure for random effects, current options are unstructured and diagonal
step Number of EM iterations
likseq Log-likelihood EM sequence

Author(s)

Florin Vaida (fvaida@ucsd.edu) and Lin Liu (linliu@ucsd.edu)

References

Vaida, Florin and Liu, Lin, Fast Implementation For Normal Mixed Effects Models with Censored Response (submitted).

Vaida, Florin and Fitzgerald, Anthony and DeGruttola, Victor (2007), Efficient Hybrid EM for nonlinear mixed effects models with censored response, Computational Statistics and Data Analysis, 51, 5718-5730.

Examples

data(UTIdata)
UTIdata <- subset(UTIdata, !is.na(RNA))
o <- order(UTIdata$Patid, UTIdata$Fup)
UTIdata <- UTIdata[o,]
cens = (UTIdata$RNAcens==1)+0
y = log10(UTIdata$RNA)
X = cbind((UTIdata$Fup==0)+0, (UTIdata$Fup==1)+0, (UTIdata$Fup==3)+0, (UTIdata$Fup==6)+0, (UTIdata$Fup==9)+0, (UTIdata$Fup==12)+0, (UTIdata$Fup==18)+0, (UTIdata$Fup==24)+0)
Z = matrix(rep(1, length(y)), ncol=1)
cluster = as.numeric(UTIdata$Patid)
fit = lmec(yL=y,cens=cens, X=X, Z=Z, cluster=cluster, method='ML', maxstep=40)

[Package lmec version 1.0 Index]