Hi all,

I am new to MCMCglmm. I have a dataset where I am interested in estimating a between individual correlation of laboratory measured performance and field behavior (display rate). I have repeated measurements on individuals (ranging from 1 to 6 measurements) for field behavior, but only a single measurement of laboratory performance for each individual. I would like to fix the residual variance for performance at near zero and test for covariance at the individual level. I also have information about whether the focal animal was interacting with another animal. I am treating this as a fixed effect. I would also like to generate a posterior distribution for the individual correlation for calculation of a CI. 

I have tried the following code for the model:

PRIOR2 = list (R = list (V = diag(c(0.1, 0.00001)), nu = 1.02, fix = 2),G = list (G1 = list (V = diag(2), n = 0.002)))

model3<-MCMCglmm(cbind(dpm,log10(endurance)) ~ trait-1 + at.level(trait, 1):interact, random=~us(trait):id, rcov = ~idh(trait):units, family=c("gaussian", "gaussian"), data=DATA, nitt=200000, thin=10,burnin=3000, prior=PRIOR2, singular.ok=T)

in this model, dpm is "displays per minute" and endurance is my singly measured laboratory performance metric. The model summary seems to indicate that I have, in fact fixed the residual variance for endurance as intended, but the model coefficients seem a little strange (identical p-values for some coefficients near p=1).

Any suggestions would be greatly appreciated.

Best,
Matt


__________________________________
Matthew E. Gifford
Assistant Professor
Department of Biology
University of Arkansas at Little Rock
2801 S. University Blvd.
Little Rock, AR 72204
501.569.3511
megifford@ualr.edu
megifford76@gmail.com
http://giffordlab.weebly.com/index.html
Twitter: @MattGifford1















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