Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size).
Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with multiple populations measured multiple times. I have limited knowledge of MCMC, so: 1) Does the specification of the prior seem sensible? 2) How do i specify rcov? Is e.g. rcov=~us(population size):units a good approach? 3) If I would like to include the other predictor in the rcov specification. Is this a good approach, rcov=~us(other predictor:population size):units? I know I could easily do this in nlme, but I'm hoping to avoid it. One reason is that I understand MCMC methods make it straightforward to assess the relative contribution of each predictor to the response. Kind regards, Atle Torvik Kristiansen ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
