Dear R-sig-phylo list,
I'm following up on a, seemingly unaddressed question from August 2013
about whether or not MCMCglmm co-estimates Pagel's lambda within a
phylogenetic regression. Seraina's original message is copied at bottom.
From what I can tell MCMCglmm doesn't co-estimate lambda, but perhaps
I'm missing something. If it does, then I would like to know how to
specify that lambda be co-estimated.
I have a complicated model with random effects apart from accounting for
phylogeny that is unable to be fit by lme(); I get an optimization error
when trying to include corPagel(fixed=FALSE) within the lme() call,
whether I specify the non-phylogenetic random effects by formula or with
pdMat constructors.
I'm analyzing plant growth of 10 species in 2 temperatures (control and
cold) over a timeline, where I have several plants per species and daily
measurements over 12 days; my model is (following lme4 formula syntax):
plant_height ~ day * temp * species + (day | ID)
My goal is to estimate/predict a growth rate for each species (while
accounting for daily variation/noise in growth rate at the individual
plant level => hence the day | ID random effect term) to then compare
the growth rates in cold versus control temperatures for each species
...to then assess which species seems most cold tolerant as measured by
growth rate difference (cold - control) and relative growth rate (cold /
control).
As an aside, I've fit the model without random effects but with
corPagel(fixed=FALSE)in gls() and lambda is estimated as equal to zero
or effectively so, depending on whether the starting value is 0 or not.
Likewise, I've fit the full mixed model without the phylogeny with lme()
and lmer() and then analyzed the phylogenetic signal of the residuals
with phylosig() in phytools and again lambda is estimated as equal to
0. So, perhaps I shouldn't worry about fitting a phylogenetic
regression in this particular case.
However, I have similar data from this and other experiments and so it
would be ideal to find a robust way of running a phylogenetic mixed
model regression with co-estimation of lambda, i.e. a way that doesn't
lead to an optimization error. Perhaps MCMCglmm offers that?
Thanks in advance for any input you could provide as to MCMCglmm and
phylogenetic signal!
Cheers,
Dan.
--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616
Original message from Seraina Graber:
Dear MCMCglmm users,
I am running a simple model corrrecting for phylogenetic
relationships using MCMCglmm. Now I am interested in the phylogenetic
signal, the analogue to Pagels lambda.
Now I have two questions:
1.) According to Hadfield and Nakagawa (2010) the analogue to lambda
(Pagel) in the mixed model approach is
var(phylo)/var(phylo)+var(residuals), however, in another conversation
about pyhlogenetic signal in MCMCglmm I found that actually
var(phylo)/var(phylo)+var(residuals)+var(random effects) is the right
measurement for the phylogenetic signal. But isnt the var(phylo) and
var(random effects) basically the same, cos actually the pyhlogeny is
the random effect in such a model? so for me rather
var(phylo)/var(phylo) + var(residuals) makes more sense.
My model:
MCMCglmm(Y ~ X random=~animal, data="" pedigree=phylotree, pr=F,
saveX=F, pl=T), X and Y are two continuous variables.
2.) Comparing to the PGLS function in caper, there the
variance-covariance matrix is adjusted for the strength of the
phylogenetic signal (estimated lambda scales the off-diagonals of the
phylogenetic vcv matrix). Is that somehow done in the MCMCglmm approach?
if yes, how?
For any help I am very grateful.
Cheers,
Sereina
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