Rémi Lesmerises <remilesmerises <at> yahoo.ca> writes: > Hi everyone, I'm running a bayesian regression using the package > MCMCglmm (Hadfield 2010) and to reach a normal posterior > distribution of estimates, I increased the number of iteration as > well as the burnin threshold. However, it had unexpected > outcomes. Although it improved posterior distribution, it also > increased dramatically the value of estimates and decrease DIC. > Here's an example:
head(spring) pres large_road small_road cab 0 2011 32 78 1 102 179 204 0 1256 654 984 1 187 986 756 0 21 438 57 1 13 5 439 # pres is presence/absence data and other variable are distance to these features # with 200,000 iteration and 30,000 burnin prior <- list(R = list(V = 1, nu=0.002)) sp.simple <- MCMCglmm(pres ~ large_road + cab + small_road, family = "categorical", nitt = 200000, thin = 200, burnin = 30000, data = spring, prior = prior, verbose = FALSE, pr = TRUE) ------------ (1) you will do much better with this kind of question on r-sig-mixed-models. (2) it looks like your chain is mixing very, very badly. If I'm reading the output correctly, it looks like your effective sample sizes for the first run (200K iterations) are 1-3 (!) -- you should be aiming for effective sample sizes of 100s to 1000s. Even with a million iterations you're only getting up to effective sample sizes of ~150 for some parameters. I would recommend (a) centring and scaling your parameters to improve mixing and (b) cross-checking with a different method (e.g. lme4 or glmmADMB) to make sure you're in the right ballpark. You shouldn't necessarily expect a Normal posterior as you increase the number of iterations; the posterior distributions are only asymptotically Normal as the number of *observations* increases. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.