Many thanks Olivia for the quick response, and apologies for not making myself clearer. I have worked through the whole 3 steps and simulated fitted values across the range of the explanatory variable in my data. That worked fine. The problem is we have been asked to supply equations by reviewers, so when we looked at the documentation where the equations are provided, it was unclear which value for psi we should use because of the x3 variable in the equation. The inverse link part was no problem, rather it was the manual calculation of joint probabilities that caused me to come unstuck. If we knew which part of the model output gives x3 (assuming log(oratio) is given by exp((intercept):3)), then I think that would solve our problem. In no way would I want to (or be able to) reengineer the prediction process! Thanks for any clarifications, and please excuse my lack of base knowledge about this kind of model. Dave -----Original Message----- From: monkeykupo(a)gmail.com [mailto:monkeykupo@gmail.com] On Behalf Of Olivia Lau Sent: May-09-12 4:50 PM To: David Auty Cc: zelig(a)lists.gking.harvard.edu Subject: Re: [zelig] Value for log(oratio) in "blogit" model I would suggest that you run through the whole 3 steps: zelig, setx, and sim -- sim will output the predicted probabilities so you can examine further, no need to re-engineer the predicted probability process. :) You can also use the predict() function in the VGAM library, which just takes the inverse link function. To find the inverse link function, take the output from vglm (say vout) and use: vout@family@inverse The inverse function takes the linear predictor (x %*% beta), but only works (well) in predict because additional objects are not called with proper lexical scoping (and are passed implicitly from the vout object itself). But if you want to learn how predicted probabilities work, I would start here. HTH. Olivia On Wed, May 9, 2012 at 1:27 PM, David Auty <auty.david.1(a)ulaval.ca> wrote: