[R] can I get same results using lme and gls?

[Douglas Bates]

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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