need help with mixed effects model
First, I want to thank everyone on this SIG list for their generous time and thoughts on my problem. I am particularly grateful for learning that my problem is "multivariate" and that lmer is not (yet) set up for that. I will certainly be paying close attention to the list for the day when it is I think it is a terrific package. Upon learning that what I am interested in is multi-variate correlation, not multi-factorial, a search of R packages turned up CORREP, which seems tailor made to my situation. In fact, perhaps once again a Nobel prize has slipped through my grasp as I find that yet another great idea of mine is not very original after all ;) So, I'll give lmer a rest for now and see how CORREP does, but I do look forward to hearing about lmer's multivariate capabilities in the future. Mark Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry Indiana University School of Medicine 15032 Hunter Court, Westfield, IN 46074 (317) 490-5129 Work, & Mobile & VoiceMail (317) 204-4202 Home (no voice mail please) mwkimpel<at>gmail<dot>com ******************************************************************
David Duffy wrote:
On Wed, 5 Mar 2008, Nick Isaac wrote:
You can do SEM-type models using the smatr package. Only drawback is that you'd have to treat Rat as a fixed effect. This is a general class of problem that afflicts several of my current projects, and I'm having a tough time choosing between mixed effect and structural equation models. The former is most appropriate for partitioning the variance, but the latter is most appropriate for modelling error variance in the observations. I don't see an obvious solution with the available tools and would appreciate any general insights. Cheers, Nick
I don't know if smatr supports multiple groups (so you can have unbalanced
type data). We generally use the Mx SEM package, which allows both
multiple groups as well as "irregular" data, and our main interest is always
in partitioning the variance. Every mixed model (indeed
every linear model) is expressible as a SEM (it just runs slower ;)).
Generally, if you want to include a random effect you set up a latent
variable with appropriate path(s) to the observed value(s). Your
relationships will be between the latent variables. Often, you'll fix
the path coefficient to unity. Have a look at Mx book/manual (Mike Neale and
Hermine Maes) and you'll get some ideas.
For the expression example, one model would be something like
+--------------------------+
Strain1-Rg1 | |
-------------------------- Rat16 |
| | | |
r1 | r1 |
Latent Gene111 <--> Gene112 Gene161 <--> Gene162
/b1 | \b3 b4/ | \ /b1 | \
Manif Tissue1 T2 T3 Tissue1 T2 T3
With appropriate equality constraints (r1=r1..=r1, b1=b1..) or
random regressions on the path coefficientsi and covariance matrix of latent
variables. So this model states (and
can test) that tissue expressions are imperfect measures of
overall gene expression in the ith animal.
But after saying that, for this particular example, I would use a
program such as ASREML or Wombat or MENDEL that easily fit multivariate
mixed models. Our group has used ASREML to fit mixture models to
deal with error in DNA pooling experiments for 50000 SNPs.
Hope that's clear (and even right), David Duffy.