[R-sig-ME] Multivariate mixed models

[Jarrod Hadfield]

Hi,

The model cannot be fitted in lmer because a multivariate error  
structure cannot be fitted.  You can do it in asreml-R which I believe  
is now free to academic users on windows.

Cheers,

Jarrod


Quoting "Ingleby, Fiona" <fci201 at exeter.ac.uk> on Sat, 22 Sep 2012  
14:10:13 +0000:

> Hi everyone,
>
> I have a dataset with 3 trait measurements for individuals from  
> different families, like this:
>
> trait1      trait2      trait3      family
>  0.5        -0.2         0.2         A
>  0.2         0.7        -0.1         A
>  0.3        -0.3         0.5         A
>  0.1        -0.1         0.4         B
> -0.4         0.5        -0.6         B
> -0.1         0.8         0            B
> -0.2         0.7         0.5         C
>
> ...and so on (there are 80 families with 20 individuals measured from each).
>
> The model I want to fit is pretty straightforward, with the three  
> traits as a multivariate response, and family as a random effect. I  
> want to be able to extract the genetic variance-covariance matrix  
> for the set of 3 traits. I can do this quite easily with MCMCglmm,  
> and am aware of non-Bayesian methods in other programs (i.e. SAS),  
> but awkwardly, I want to fit a non-Bayesian model in R.
>
> I can run individual lmer models for each trait, e.g.:
>
> model <- lmer (trait1 ~ 1 + (1|family) )
>
> and extract the random effects variance for the family term, giving  
> me the genetic variance for each trait from each model individually.  
> But this doesn't allow me to model the genetic covariance between  
> traits, and so I'm wondering if there is a way to run lmer with a  
> multivariate response, or if there is another method I could use to  
> fit this kind of model?
>
> I did try re-arranging my dataset so that the 3 trait scores for  
> each individual were stacked into one variable (creating new  
> variables for trait.id (1, 2, or 3) and individual id within family)  
> like this:
>
> trait.score     trait.id     individual     family
>
> and then the model:
>
> model <- lmer (trait.score ~ 1 + (trait.id|family) + (individual|family) )
>
> but this model makes R crash and so I'm assuming there might be some  
> kind of convergence problem?! And I'm not even sure this model would  
> give me the genetic covariances between traits, it would probably  
> just give the genetic variances for each trait (as before but in one  
> model instead of 3 separately)?
>
> Any ideas/advice would be greatly appreciated.
>
> Thanks,
>
> Fiona Ingleby
>
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>



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