Take a look at lmekin() in the coxme package. The motivating data set for my development
of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families.
The random effect is an intercept per subject with sigma^2 K as its variance where K is
the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice,
etc). lmekin is a linear models front end to the same underlying routines.
I think you want lmekin(y ~ x1 + x2 + (1| subject), data=yourdata, varlist=
D)
or some such, where D is the similarity or "correlation" form of you distance
matrix.
A downside is that lmekin is sort of the poor cousin to comxe -- with finite time I've
never gotton around to writing predict, residuals, plot, ... methods for it. The basic
fit is fine though.
Terry Therneau
(In general I agree with Bert & Ben to try the other list, but I don't happen
to read it.)
On 06/04/2013 05:00 AM, r-help-requ...@r-project.org wrote:
Hi,
I'm trying to build a mixed-effects model in which I'd like to include
either a distance matrix or a phylogenetic tree as a random effect.
The troubles I've had are that:
1. Function lmer() in package lme4 only accepts a data frame column as a
random factor and not a distance matrix.
2. Function MCMCglmm() in package MCMCglmm only accepts a rooted and
ultrametric phylogenetic tree as a pedigree argument while my tree is
neither (and for various reasons I cannot construct one or coerce mine
to be a rooted, ultrametric tree).
Is there any way around it?
I'd appreciate mostly a solution to problem 1.
Roey
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