[R-sig-ME] A mixed effect regression tool based on bayesian priors like bayesglm

[Phillip Alday]

Sidestepping your question a bit, you might want to consider the
rstanarm package instead of blme because it is more directly in the
Gelman/arm tradition/school of thought. You can move your arm::bayesglm
models to rstanarm::stan_glm and then use rstanarm:stan_glmer for the
mixed model. But only *you* can know / justify which priors you should
use -- default priors are just that, default and they may not always be
sensible for every case.

Phillip

On 05/17/2017 10:46 AM, Angelo D'Ambrosio wrote:
> I use extensively Gelman's bayesglm for the every day use due to the great
> stability of the estimates especially in the case of separation.
> 
> I needed an equivalent of empirical bayesian regularization for glm mixed
> effect models. These models are strongly influenced by extreme conditions
> (like conditional probabilities of zero and separation) and like usual
> logistic regression model they fail in these cases.
> 
> I found the blme package that does exactly what I need, solving the
> separation problem. Now the problem is to set it up in order to be work
> exactly as bayesglm, in order to achieve consistency in my analysis.
> Reading Gelman paper on bayesglm() I understood I should use a t
> distribution with 1 df (eg. Cauchy) and 2.5 scale, rescaling inputs:
> 
>     bglmer(Out ~ arm::rescale(Pred) + (1 | PatientID), family = binomial,
> Data.events, fixef.prior = t(df = 1, scale = 2.5))
> 
> Is it correct? My doubt is what to do with the cov.prior parameter; should
> I leave it as default (wishart) or should I put it to NULL? Also in
> Gelman's paper it is said that the intercept should have the same prior
> distribution but with scale 10, and I don't know how to specify a different
> prior for it.
> 
> Also, I'm starting to think that bayesglm doesn't rescale the inputs
> directly but scales the prior distribution according to the inputs. Am I
> right?
> 
> Can you help me with this?
> 
> Thanks
> 
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