[R] Bayesian logistic regression?
Spencer Graves
spencer.graves at pdf.com
Fri Jun 23 17:35:14 CEST 2006
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
>
More information about the R-help
mailing list