Hi, Daniel lamda and H^2 are equivalent as we say in Hadfield and Nakagawa (2010) or said also in Housworth et al (2004)
Housworth E, Martins E, Lynch M (2004) The phylogenetic mixed model. Am Nat 163(1):8496. lamda = var(phylo)/(var(phylo)+var(residuals)) A mathematical proof of this is in Hansen and Orzack 2005 Hansen TF, Orzack SH (2005) Assessing current adaptation and phylogenetic inertia as explanations of trait evolution: the need for controlled comparisons. Evolution 59(10):20632072 More general verison of H^2 or lamda is lamda = var(phylo)/(var(phylo)+var(all other random effects + residuals)) (probably not including measurment errors) Best wishes, Shinichi On 24/07/2014, at 10:51 am, Daniel Fulop <dfulop....@gmail.com<mailto:dfulop....@gmail.com>> wrote: 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 _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org<mailto:R-sig-phylo@r-project.org> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/ ________________________________ Shinichi Nakagawa, PhD (Associate Professor of Behavioural Ecology) Department of Zoology University of Otago 340 Great King Street P. O. Box 56 Dunedin, New Zealand Tel: +64-3-479-5046 Fax: +64-3-479-7584 http://sparrow.otago.ac.nz/ [[alternative HTML version deleted]]
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