Hi everyone, I am running a logit.bayes model and I get the results that closely resemble regular logistic regression. I need to determine the importance of predictors. I want to try and determine which coefficients are more "powerful." I have ordered variables, numeric as well as binary as predictors. I attempted to standardize these using the rescale function from arm package but still the process fails for ordered variables (coefficients do not seem to be standardized). In general standardizing variables to obtain beta coefficients may not be the most efficient way to determine the importance of variables. Another way to do this is to use an model information value or similar value. For example, DIC (or R^2 in traditional regression). I know that rjags does report DIC but I would have to build the models manually there. Is there a way to include DIC for zelig bayesian models? If anyone has an alternative way to determine the importance of predictors when predictors are on different scales, I would love to hear about it. Thanks, Michael