I think your best chance is to use MCMCglmm().

On 12/09/13 23:24, Justice Moses K. Aheto wrote:
> Dear All,
> I have 3 outcome variables which are continuous. There are 4 predictor variables comprised of both continuous and categorical variables.
> I am jointly modelling the 3 outcome variables (az, hz and wz) in R with the codes below:
>
>
> M13<-lm(cbind(az, hz, wz) ~ 1+v4new+hw+mnew+v10new , data=dataF)
> summary(M13)
> anova(M13)
>
>
> My next task is to extend this model to mixed effect model (multilevel). I used the lmer but the output is not what I expected. Upon reading the literature on lmer, it does not fit multivariate multiple regression with extension to mixed effects models so I suspect that as the problem. Below is the command:
>
>
> M21<-lmer(cbind(az, hz, wz) ~ 1+v4new+hw+mnew+v10new , (1 | hhid), data=dataF)
> summary(M21)
> summary(manova(M21))
>
> With this, I am only able to get estimates for the combined outcome variables against the predictors instead of obtaining estimates on how each predictor contributes to each outcome variables and to conduct possible test on each coefficient across the outcome variables.
> Please, any help will be highly appreciated.
> Many thanks.
>   
> Kind regards.
>   
> Justice Moses K. Aheto.
> PhD Candidate in Medicine (United Kingdom)
> MSc Medical Statistics (United Kingdom)
> BSc Statistics (Ghana)
> HND Statistics (Ghana)
>
> (Chief Executive Officer)
> Statistics & Analytics Consultancy Services Ltd.
> E-mail: justiceaheto@yahoo.com
> Mobile:0044(0)7417589148 (United Kingdom)
>               00233(0)509914602 (Ghana)
> 	[[alternative HTML version deleted]]
>
>
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


	[[alternative HTML version deleted]]

