MCMCglmm - Random effect prior specification
Hi Tanya, The warning is because MCMCglmm augments the data set with missing data for missing combinations of rep/sex. This is just an algorithmic trick to keep the effects balanced and therefore easier to Gibbs sample. It is not an warning the user really has to worry about. However, if the rep 1 in males and females have no connection, except by name, do you really expect their to be a between-sex covariance in their effects. If not, probably better to use idh(sex):rep. However, with so few reps it will not be possible to get precise estimates of the variance of their effects, and the posterior will be sensitive to alternate prior specifications. That being said, if the rep effects are not of immediate interest this might not impact on the rest of the analysis. You could also fit them as fixed effects. Autocorrelation is not an issue per se, it just means you have to collect more samples to get the same reduction in Monte Carlo error. You should focus on the effective sample size and aim to get something in the region of 1-2 thousand effective samples. Cheers, Jarrod Quoting Tanya Pennell <T.Pennell at sussex.ac.uk> on Fri, 15 Nov 2013 10:06:23 +0000:
Hi, I'm currently running an MCMCglmm for a data set of male and female fitness within 100 genetic fly lines. For each line, I have 4 female data points and 6 male data points. Each data point represents the average fitness of that sex in each replicate (note that reps are labelled 1-4 for females and 1-6 for males, and each rep for the sexes was carried out at different times - i.e. rep 1 female was done at a different time to rep 1 male). For the model, I therefore need to incorporate replicate by sex as a random effect: prior.model.2<-list(R=list(V=matrix(c(400,0,0,600),2,2), nu=0.01),G=list (G1=list(V=matrix(c(400,0,0,600),2,2), nu=2, alpha.mu=c(0,0), alpha.V=matrix(c(400,0,0,600),2,2), G2=list(V=matrix(c(400,0,0,600),2,2), nu=2, alpha.mu=c(0,0), alpha.V=matrix(c(400,0,0,600),2,2)))) model.2 <- MCMCglmm(S_relative_fec ~ sex-1, random=~us(sex):line + us(sex):rep,rcov=~idh(sex):units, family="gaussian", nitt = 100000, burnin = 30000, thin=30, data = h100newdata, prior = prior.model.2, verbose = FALSE) However, when I run this model I get the following warning message: 'some combinations in us(sex):rep do not exist and 2 missing records have been generated' The autocorrelation for female rep and units is also very high Does anyone know how I can correct the model or prior to change this? Many thanks, Tanya [[alternative HTML version deleted]]
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