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

Ben Bolker bbolker at gmail.com
Thu Feb 26 01:12:14 CET 2015

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  I haven't actually tried any problems like this, but

1. in principle this is possible
2. There's a hack at
3. you might take a look at the pedigreemm package for another
example.  There *might* be something else in the Reverse
Depends/Suggests list at
http://cran.r-project.org/web/packages/lme4/index.html , but nothing
jumps out at me.

  Steve Walker is in the very early stages of working on a
phylogenetic model with a similar structure.

  Looking forward to seeing what other people have to say ...


On 15-02-25 06:42 PM, Matthew Keller wrote:
> 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!
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