[R-sig-ME] estimating variance components for arbitrarily defined var/covar matrices

[Anthony R Ives]

Matthew,

You should be able to do this in communityPGLMM in {pez}. Also, Steve Walker is currently working on a way to do this in lmer/glmer.


Cheers, Tony


On 02/26/15, Jarrod Hadfield  wrote:
> Hi Matthew,
> 
> Both MCMCglmm and asreml-r fit these models in R.
> 
> Cheers,
> 
> Jarrod
> 
> 
> 
> 
> Quoting Matthew Keller <mckellercran at gmail.com> on Wed, 25 Feb 2015 16:42:32 -0700:
> 
> >Hi all,
> >
> >This is a typical problem in genetics and I'm trying to figure out whether
> >there's any way to solve it using lmer or similar, and if not, why it isn't
> >possible.
> >
> >Often in genetics, we have an n-by-n matrix (n=sample size) of genetic
> >relationships, where the diagonal is how related you are to yourself (~1,
> >depending on inbreeding) and off-diagonals each pairwise relationship. I'd
> >like to be able to use lmer or some other function in R to estimate the
> >variance attributable to this genetic relationship matrix. Thus:
> >y = b0 + b*X + g*Z + error
> >where y is a vector of observations, b is a vector of fixed covariate
> >effects and g is a vector of random genetic effects. X and Z are incidence
> >matrices for b & g respectively, and we assume g ~ N(0, VG). The variance
> >of y is therefore
> >var(y) = Z*Z' * VG + I*var(e)
> >
> >Z*Z' is the observed n-by-n genetic relationship matrix. Given an observed
> >Z*Z' genetic relationship matrix, is there a way to estimate VG?
> >
> >I guess this boils down to, if we have an observed n-by-n matrix of
> >similarities, can we use mixed models in R to get the variance in y that is
> >explained by that similarity?
> >
> >Thanks in advance!
> >
> >	[[alternative HTML version deleted]]
> >
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> 
> 
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