Hi,
I have run 4 level models with binary response variable using mcmc.
I have added context variables - one at a time - for each level.
Some questions:
1. Is it straght forward to change the coefficients from odds to probability in such a model?
2. To be able to compare the strenght of the different explanatory context variables that are
introduced at the same level one at a time (in separate models): do I have to standardize the variables in advance?
And how do I do this in such a model? And what do comparsion of the DICs actually tell me?
I have tried to make a direct comparsion between the coefficents in the different models to be able to say which of them explain most. I did that by comparing the size of the coefficients (the ones who were significant) - but I tested it without standardising
and without turning them into probabilities. But I'm not sure if this is the right way to do it?
I have also tried to interpret the DICs (I'm using mcmc) for each model - again to try to say something about which
variable explains the most of the variation in the binary outcome variable (lower DIC = better fitted model and less complexity = explain most?). But I have a feeling that this it not right?
I hope someone have any ideas on how to do this, or mayby where I may find the answer.
All the best,
Ingar