Hello Below are responses to questions I posed about covariates, priors and model selection. It seems there are few ways to choose the best model in WinBugs. The way I am approaching this problem is somewhat Bayesian-like in that I look at how the univariate model behaves in terms of the density and history plots for beta, clustering and heterogeneity components of the model. Depending on or conditional on which univariate models have sig. values for beta and good history and density plots for unknown paramters, I use them to contruct the full model. In this way DIC was reduced and the model behaved well in terms of exploring the paramter space, mixing of markov chains, etc. I have not found any reason to reject nor accept this method as a valid one since no literature to my knowledge exists about it yet! So any comments or criticisms are welcome. Thanks Ayaz Thanks to Dr. Finn Krogstadt and Dr. Glen D. Johnson for responses below. Question I asked was how to prioritize selection of models either by lower DIC or better history plots and other output from WinBugs of parameter space. WRT #2, your DICs are essentially equal, so choose the model with the stable "time series" (I believe you mean the "trace plots" for monitoring convergence and behavior of a Markov Chain wrt to your coefficient beta). Also, if you are talking about traces, then you should be monitoring at least 3 independednt Markov Chains and run the iterations until all of the traces converge, overlapping each other and varying like white noise with no trend. The Gelman-Rubin diagnostics were designed to monitor aspects of these traces in order to assess convergence, but just looking at the traces will pretty much tell you if you have convergence. Also, I caution you, given that I've worked alot with these hierarchical spatial Poisson models, convergence does not happen readily for linear coefficients associated with covariates. It works great for producing smoothed response variables, but not for making inference about these linear coefficients. > 3. IS THIS APPROACH VALID? --> > I ran univariate models for 12 variables using 3 different > priors which makes 36 models in total. Full model was chosen > based on following > criteria: > -beta value for covariate is significant > -tau.h and tau.c for non-spatial and spatial random effects, > respectively, and beta value of covariate have a time-series > plot which shows good mixing and low variation, i.e. not a > very narrow band of values with large spikes in the estimate > occuring randomly throughout the simulation. Ayaz, For model selection, there really is no substitute for the model likelihood or posterior probability. You might want to take a look at the PINES example (in some example sets but not others). ------------------------------------------------------------------- This list is for discussion of modelling issues and the BUGS software. For help with crashes and error messages, first mail [log in to unmask] To mail the BUGS list, mail to [log in to unmask] Before mailing, please check the archive at www.jiscmail.ac.uk/lists/bugs.html Please do not mail attachments to the list. To leave the BUGS list, send LEAVE BUGS to [log in to unmask] If this fails, mail [log in to unmask], NOT the whole list