Hi Erika,
I have not tried this before, and I hope that somebody will correct
me if I'm wrong, but the glmer function in the lme4 library appears to do
what you want. From examples(lmer):
lmer> (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |
herd), family = binomial, data = cbpp))
...I guess that this will do what you want it to because it has multiple
variables on the LHS and both continuous and categorical variables on the
RHS, along with an explicit grouping structure.
In your case, you probably want to leave the family= argument out, as noted
in ?glmer, "If 'family' is missing then a linear mixed model is fit;
otherwise a generalized linear mixed model is fit." ...MANCOVA tend to be
generalized linear models.
Once again, though, I have not used this system personally, haven't seen
your data, and don't know what output to expect. Hopefully somebody else can
confirm or deny this solution's efficacy.
--Adam
On Mon, 8 Sep 2008, Erika Crispo wrote:
Hello,
I need to perform a mixed-model (with nesting) MANCOVA, using Type III
sums of squares. I know how to perform each of these types of tests
individually, but I am not sure if performing a mixed-model MANCOVA is
possible. Please let me know.
Erika
<>< <>< <>< <>< <>< <>< <><
Erika Crispo, PhD candidate
McGill University, Department of Biology
http://www.biology.mcgill.ca/grad/erika/index.htm
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