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 <pda...@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....@cbs.dk Priv: pda...@gmail.com > > [[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.
