[R] Bayesian logistic regression?

[Spencer Graves]

	  I don't know of anything.  A brief search using RSiteSearch("Bayesian 
logistic regression") and RSiteSearch("Bayesian regression") led me to 
the BMA package plus several MCMC solutions (coda, MCMCpack, and 
BayesCslogistic {cslogistic}).  If it were my problem, I might spend a 
few minutes with BMA and then probably write my own.

	  I would like to see a (possibly singular) multivariate normal (or 
normal + inverse gamma) "prior" as an optional argument for lm and glm 
and when present would produce the obvious "posterior" [exact for lm and 
approximate for glm] as an attribute of the output.  A few years ago, 
wrote something to do this that would do ordinary least squares one step 
at a time and get the standard OLS answer (starting from a 
noninformative norma + inverse gamma prior).  From this, it is a short 
step to Kalman filtering:  Just add an appropriate "decay" function to 
increase the uncertainty to convert the posterior at one step into the 
prior for the next.

	  I'm sure this didn't help much other than confirm that your own 
search did not overlook something obvious.

	  Best Wishes,
	  Spencer Graves

Andrew Gelman wrote:
> Hi all.
> Are there any R functions around that do quick logistic regression with 
> a Gaussian prior distribution on the coefficients?  I just want 
> posterior mode, not MCMC.  (I'm using it as a step within an iterative 
> imputation algorithm.)  This isn't hard to do:  each step of a glm 
> iteration simply linearizes the derivative of the log-likelihood, and, 
> at this point, essentially no effort is required to augment the data to 
> include the prior information.  I think this can be done by going inside 
> the glm.fit() function--but if somebody's already done it, that would be 
> a relief!
> Thanks.
> Andrew
>