On 5/20/07, toby909 at gmail.com <toby909 at gmail.com> wrote:
> I was wondering how to get the same results with gls and lme. In my lme,
the
> design matrix for the random effects is (should be) a identity matrix and
> therefore G should add up with R to produce the R matrix that gls would
report
> (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G
and say H
> (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am
having trouble
> finding the right corresponding specification for the gls.
Thanks for including a reproducible example. However, I'm a bit at a
loss as to why you would want to try to create a gls model that fits a
mixed-effects model that has random effects for intercept and slope at
two nested levels. I don't think that corCompSymm will do what you
want but, to tell the truth, I have difficulty in thinking of the
model in that form. I much prefer the mixed-effects form.
>
> Thanks for your help.
>
> Toby
>
>
> dtaa >
read.table("http://www.ats.ucla.edu/stat/mplus/examples/ma_snijders/mlbook1.dat",
> sep=",")
> dta1 = reshape(dtaa, list(c("V10","V12")),
"score", direction="long",
>
drop=c("V2","V3","V4","V5","V6","V7","V8","V9","V11","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25"))
> colnames(dta1)[1] = "schoolNR"
> dta2 = dta1[order(dta1$id),]
> head(dta2)
> timef = factor(dta2$time)
>
> summary(mdl1l <- lme(score~timef-1, dta2,
~timef-1|schoolNR/id,,,,"ML"))
> summary(mdl1g <- gls(score~timef-1, dta2, corCompSymm(,
~timef|schoolNR/id),
> varIdent(, ~1|id*timef),,"ML"))
>
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