[R-sig-ME] Multivariate Multilevel Model with lmer()

[Christopher T. Moore]

Hello,

I'm trying to use lmer() to replicate Snijders and Bosker's multivariate
multilevel model examples 13.1 and 13.2, which they estimated with MLwiN.
My code (see below) replicates the fixed effects and between-schools
variance estimates in the book, but not the within-schools variances.

I think I need to suppress the residual variance estimate so that level 1
defines the multivariate structure and the level 2 variances become the
residual between-pupil variances. Is that possible with lmer()? Any such
options in the nlme package?

I searched the help files and the R-help archives but couldn't find a
solution. Thanks in advance.

Regards,
Chris

--
Christopher T. Moore, M.P.P.
Doctoral Student
Quantitative Methods in Education
University of Minnesota
44.9785°N, 93.2396°W
moor0554 at umn.edu
http://umn.edu/~moor0554


####################
#Attempt to reproduce Snijders and Bosker's examples 13.1 and 13.2
library(lme4)
library(reshape)
download.file("http://www.ats.ucla.edu/stat/mplus/examples/ma_snijders/mlbook1.dat",
destfile="mlbook1.dat")
data.mlbook1 <- read.csv("mlbook1.dat")
names(data.mlbook1) <- c("schoolNR", "pupilNR", "IQ_verb", "IQ_perf",
"sex", "Minority", "repeatgr", "aritPRET", "classNR", "aritPOST",
"langPRET", "langPOST", "ses", "denomina", "schoolSES", "satiprin",
"natitest", "meetings", "currmeet", "mixedgra", "percmino", "aritdiff",
"homework", "classsiz", "groupsiz")
data.mlbook1$IQ_verb <- scale(data.mlbook1$IQ_verb, scale=F)
data.mlbook1$groupsiz <- scale(data.mlbook1$groupsiz, scale=F)
data.mlbook1$schoolNR <- factor(data.mlbook1$schoolNR)
data.mlbook1$pupilNR <- factor(data.mlbook1$pupilNR)
data.mlbook1 <- ddply(data.mlbook1, "schoolNR", transform,
schIQV=mean(IQ_verb))
data.mlbook1 <- melt(data.mlbook1, measure.vars=c("aritPOST", "langPOST"))
str(data.mlbook1)
model.empty <- lmer(value ~ - 1 + variable +
  (- 1 + variable | schoolNR / pupilNR), data.mlbook1, REML=F)
print(model.empty, correlation=F)
model.full <- lmer(value ~ - 1 +
  variable * IQ_verb - IQ_verb +
  variable * schIQV - schIQV +
  variable * groupsiz - groupsiz +
  variable * I(IQ_verb * groupsiz) - I(IQ_verb * groupsiz) +
  (- 1 + variable | schoolNR / pupilNR), data.mlbook1, REML=F)
print(model.full, correlation=F)
####################