As a matter of principle, yes, multivariate mixed models do exist, look at the
last bit of example(manova) (in reasonably recent versions of R).
In practice, it often doesn't really buy you much. It just gives a joint
test for all the DVs, the estimates are the same as in separate analyses.
The tricky bit is usually to define precisely what the research question is: Do
you want to know whether the predictors affect the marginal distributions of Y1,
Y2,... or are you interested in conditional effects given other DVs (aka test
for additional information)? The latter case leads to regression models where
other DVs are entered as covariates.
There's no issue with having categorical variables as predictors in multiple
regression in R, dummy variables are created internally. But if you are
considering mixed models, presumably you have a random effect that needs to be
included?
-pd
On Oct 9, 2013, at 10:23 , laurie bayet wrote:
> Hi,
>
> Sorry to bother you again.
>
> I would like to estimate the effect of several categorical factors (two
> between subjects and one within subjects) on two continuous dependent
> variables that probably covary, with subjects as a random effect. *I want
> to control for the covariance between those two DVs when estimating the
> effects of the categorical predictors** on those two DVs*. The thing is, i
> know the predictors have an effect on DV1, and i know DV2 covaries with
> DV1, so it would be "cheating" to simply estimate the effect of
the
> predictors on DV2 because those effects could be indirect (via DV1), right
?
>
> I see two solutions :
>
> *One solution would be a mixed model MANOVA (if that even exists)*. But i
> don't know how to run a mixed model MANOVA, i tried to do it with
> Statistica but couldn't find the right module (I know how to declare
two
> DVs and run a GLM, but *I don't know if the covariance between my two
DVs
> is automatically controlled for*). Same thing with R. I tried to ask a
> question on Statistica's forum with no answer, tried looking around in
the
> manuals with no improvement.
>
> *A backup solution would be a multiple regression* (regressing DV2 against
> DV1 with the categorical predictors). But i am not sure how to implement a
> mixed model, which function i should use and besides, it would be *much
> less convenient because one of my categorical predictors has three
> levels*(so i would have to split it and make it two predictors,
> right?).
>
> Thank you for any help at all !
>
> Cheers,
>
> L.
>
> [[alternative HTML version deleted]]
>
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--
Peter Dalgaard, Professor
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com