Dear list members,
I am attempting to run a multiresponse model to investigate factors
influencing the evolution of a trait measured in both males and
females of multiple species. Therefore I chose a multiresponse
approach using MCMCglmm, but my model has a series of little caveats
(don?t they all?) that I?d like to make sure I?m addressing
correctly. Here is a brief description of the model I?m trying to fit:
Ymale, Yfemale: the same response variable, measured in males and females
X1: a variable (factor, 2 levels) that I expect to affect both sexes
differently
X2: a variable (factor, 2 levels) that I expect to affect both sexes
equally (or estimate the joint effect across sexes)
X3: a continuous variable measured only for males (which I therefore
expect to only have an effect in male measurements)
so I am currently specifying the model as below:
mod <- cbind(Ymale, Yfemale) ~ trait*X1 + X2 + at.level(trait, 1):X3
ainv <- inverseA(phylo)$Ainv
Prior <- list(R=list(V=diag(2), nu=0.002), G=list(G1=list(V=diag(2),
nu=0.002)))
result <- MCMCglmm(mod,
random = ~us(trait):spp, rcov = ~idh(trait):units,
data=dat, prior = Prior, family = rep(?gaussian?, 2),
nitt=2100000, burnin=100000, thin=1000)
with that, I have a couple questions (besides: does this model
specification look reasonable?):
1. Prior: I wanna make sure I?m specifying an inverse-gamma with
scale=shape=0.001 for the variance components. Based on footnote 1
of the Course Notes (p.102) I think I am based on the changes, but
I?ve seen several times in mailing lists responses and other papers
nu=1.002 being used for a matrix of same dimensions. My results are
robust to either specification but I?d like to make sure I?m doing
what I think I?m doing, given that there have been changes on this
regard.
2. I am calculating the correlation between ymale and female as:
result$VCV[,2]/sqrt(result$VCV[,1]*result$VCV[,4])
as described in the tutorial vignette. My values, however, are
extremely high. The two traits are indeed strongly correlated among
species, but I?m getting credible intervals of 0.96 - 0.99 (for
comparison, a raw non-phylogenetic correlation of the traits is
0.75). Am I forgetting to include any variance components to
calculate the intersexual correlation? Is this increase in the
correlation expected given the inclusion of the fixed and random
effects in the model?
3. I have a hypothesis that the correlation between male and female
traits should be stronger for one level of X1 than for the other. Is
it possible to specify random terms in order to calculate
(co)variances of the traits conditional on X1?
I deeply appreciate any help you can provide. Cheers!
Abra?os,
Rafael Maia
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http://www.rafaelmaia.net/
PhD Candidate, Integrated Bioscience
University of Akron
"A little learning is a dangerous thing; drink deep, or taste not
the Pierian spring." (A. Pope)