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How to constrain varcov of 2 random effects so that correlation between random terms is 1 and only one global variance is estimated?
2 messages · Alexandre Courtiol, Jarrod Hadfield
Hi Alexandre, It sounds like you want to fit something called a multi-membership model. Imagine a data set set out like: y A B 1 a b 0 a c 2 c b 0 b a 3 a d you want the effect of a in A to equal the effect of a in B, such that the random effect model for the 1st and 4th row are identical. Is this correct? If so the covariance matrix for the random effects should actually be [V,V,V,V] rather than [V,1,1,V]. I'm not sure if multi-membership models can be fitted in lmer but some earlier posts suggest not. In MCMCglmmm you can fit this model by fitting random=~idv(mult.memb(~A+B)) Note that this only fits a single variance V. A and B have to be factors with the same levels, although it is not necessary that all levels are represented in each factor. In the example above, d only appears in B, but the analysis will still work as long as d is a level in A. i.e. factor(A, levels=union(A,B)). The initial set up may be slow because the design matrices are not treated as sparse, but once it is MCMCing in C++ it is working with sparse matrices and should be relatively speedy. Cheers, Jarrod Quoting Alexandre Courtiol <alexandre.courtiol at gmail.com>:
Dear all, I need to do a linear mixed model and I want to constrain the varcov matrix of random effects. Let's say I have 2 random effects A and B, I want the matrix to be [V,1,1,V], i.e. V=VA=VB, and 1=cor(A,B). Both random effects are factors with the same levels. Is there any easy way to do that in lme, lmer, mcmcglmm or something else? (I don't care so much about the package to use as long as I can do that). Thanks a lot. -- Alexandre Courtiol http://sites.google.com/site/alexandrecourtiol/home [[alternative HTML version deleted]]
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