m.fenati <at> libero.it <m.fenati <at> libero.it> writes:
> > Dear R users, > I’d like to pose aquestion about pMCMC and HDP. > I have performed a mixed logistic regression by > MCMCglmm (a very good package) > obtaining the following results: > [snip] > > post.mean l-95% CI u-95% CIeff.samp > ID_an 0.7023 0.0001367 3.678 2126 > > R-structure: ~units > > post.mean l-95% CIu-95% CI eff.samp > units 1 1 1 0 > > Location effects: febbreq~ as.factor(sex) > > post.mean l-95% CIu-95% CI eff.samp pMCMC > (Intercept) -3.6332 -5.6136 -1.7719 3045 <2e-04 *** > as.factor(sex)M -2.9959 -6.0690 0.1969 2628 0.0455 * > --- > Signif. codes: 0 ‘***’0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > As you can see, pMCMC for gender is just less than 5%, but the credible > interval (HPD) is wide and includes the 0 value. > How can I interpret these different results? My first suggestion would be to post this sort of question to the r-sig-mixed-models list instead (I would forward it but I'm replying via Gmane) I haven't been able to find much information on the definition of the Bayesian p-value, but looking into the code of summary.MCMCglmm you can see: solutions <- cbind(colMeans(object$Sol[, 1:nF, drop = FALSE]), ## means coda::HPDinterval(object$Sol[, 1:nF, drop = FALSE]), ## HPD effectiveSize(object$Sol[, 1:nF, drop = FALSE]), ## eff sample size 2 * pmax(0.5/dim(object$Sol)[1], pmin(colSums(object$Sol[,1:nF, drop = FALSE] > 0)/dim(object$Sol)[1], 1 - colSums(object$Sol[, 1:nF, drop = FALSE] >0)/dim(object$Sol)[1]))) That is, it's basically the minimum of the proportion of samples that are on one side or the other of zero. I would suggest looking at density plots to see what might be happening: library(coda) xyplot(mcmc(modelfit$Sol)) ______________________________________________ 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.