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Hello, I am comparing the fit of two random effects models (one linear, one gamma with log link) and it seems that the gamma model does not converge that well (higher autocorrelation, chain is not mixing that well for one of the coefficients etc.) but the DIC for the gamma model is lower than the one from the linear model (30414.300 vs. 32470.800). Also gamma model is known to make more sense for the data I am using due to skewness, heteroscedasticity etc. I am a bit unclear of how to interpret these results, and the fact that gamma model has a lower DIC but it does not converge that well. Does this mean that I should keep looking for a different model achieving better convergence and low DIC? Any suggestion (as well as any useful, enlightening reference) would be appreciated. Thanks, Nicholas -- Nicholas Mitsakakis, MSc, PhD Research Associate - Biostatistican Toronto Health Economics and Technology Assessment (THETA) Collaborative Leslie L. Dan Pharmacy Building, University of Toronto 6th Floor, Room 658 144 College Street Toronto ON, M5S 3M2 e: [log in to unmask] t: (416) 946 - 3700 f: (416) 946 - 3719 w: www.theta.utoronto.ca ------------------------------------------------------------------- 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