Hi;

If I understood, you can create a copy of the variable and rename levels,
jointing them and building a model with the levels merged. Then you compare
the models and find if the models are or are not different (p<0.05), if
they are ... your levels are different.

I also know that Ronaldo Reis Júnior has a source file that make this
comparisons. His website is:
http://www.ppgcb.unimontes.br/user/view.php?id=3&course=5 . Unfortunately
it is in portuguese, but there you can find his e-mail and then ask him for
further information.

Kind Regards, RSolar.

On 24 February 2012 12:53, Scott Chamberlain <scttchamberlain4@gmail.com>wrote:

> Hello,
>
>
> I have looked at the archives and I still can not figure this out.  So
> this code
>
> dat <- data.frame(var1 = as.factor(rep(c("a","b"), each=8)),
>           var2 = as.factor(rep(c("c","d"), 8)),
>           response = round(rnorm(16, 10, 4), 0))
> mod <- glm(response ~ var1 * var2, data=dat, family=poisson)
> anova(mod, test="Chisq")
>
>
>
> gives me my glm model with poisson error dist, and main effects tests, but
> I would like to compare the levels of var2 (c and d) within each level of
> var1 (a and b).  What I don't want is just c vs. d for var2 averaged across
> a and b levels of var1.
>
> I imagine multcomp is the way to go here?
>
>
> Thanks!
> --
> Scott Chamberlain
>
>
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>
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>



-- 
==========================================
Ricardo Ribeiro de Castro Solar (Curriculo
Lattes<http://lattes.cnpq.br/9924177207371692>
)
MSc. em Entomologia - Doutorando - PPG Entomologia
Laboratório de Ecologia de Comunidades/Formigas <http://www.labecol.ufv.br>
(31) 3899-4018 - Contato Skype: rrsolar
Universidade Federal de Viçosa - MG
==========================================

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