Hi Peter, and thank you for your quick and helpful reply !

"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)?"
Hmmm I think that, yes, i am looking for that additional information
(although i don't know what "marginal distributions" means). *So multiple
regression it is, thank you !*

Yes *i do have a random effect* to include (subject's number). Is it ok to
do that ? Can i do this multiple regression with, say, lmer or glmer, even
if i am not sure if the relation between the two DVs is actually linear
(can i run a multiple regression on ranks instead, should i test for a
linear correlation beforehand ?) ?

Thank you again, your answer was very helpful :-)

Best,
L.


2013/10/9 peter dalgaard <pdalgd@gmail.com>

> 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]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> --
> Peter Dalgaard, Professor
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd.mes@cbs.dk  Priv: PDalgd@gmail.com
>
>

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