[R] bayesglm with weights

[Edouard Chatignoux]

Dear list,

I have troubles with using Bayesian logistic model  with count data in bayesglm. 
If I consider the following artificial data set, with a response y and a covariate x, in a form of row data (a) and count (b) :

a<-data.frame(y=c(rep(1,10),rep(0,6)),x=c(rep(5,6),rep(4,4),rep(5,1),rep(4,5)))
a$un<-1
b<-aggregate(a$un,list(y=a$y,x=a$x),sum)
names(b)[3]<-"w"

A binomial model with glm running on a and b give the same results :
(M1=glm(y~x,family=binomial,data=a))
(M2=glm(y~x,data=b,family=binomial,weights=w))

However, with the arm and bayesglm function, the equivalent models with non-informative prior, give different results:

library(arm)
(M3=bayesglm(y~x,data=a,family=binomial,prior.scale=Inf, prior.df=Inf) )#M3=M2=M1
(M4=bayesglm(y~x,data=b,family=binomial,weights=w,prior.scale=Inf, prior.df=Inf))#M4 is different

When I try a formulation with y=response rate or y=(response,failure), I get the rights coefs, but lower standard errors.

c<-data.frame(y=c(4/9,6/7),x=c(4,5),w=c(9,7))
(M5=bayesglm(y~x,data=c,family=binomial,weights=w,prior.scale=Inf, prior.df=Inf))

y<-matrix(c(4,6,5,1),ncol=2)
x<-matrix(c(4,5),ncol=1)
(M6=bayesglm(y~x,family=binomial,prior.scale=Inf, prior.df=Inf))

Am I missing something?

Thanks,


Edouard Chatignoux, Statistician
Health oservatory of the Paris Île-de-France region
43 rue Beaubourg- 75003 PARIS 
Tel. : 01 77 49 78 54 
Fax. : 01 77 49 78 61


I'm running R 2.13.0 under windows XP

> sessionInfo()
R version 2.13.0 (2011-04-13)
Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252   
[3] LC_MONETARY=French_France.1252 LC_NUMERIC=C                  
[5] LC_TIME=French_France.1252    

attached base packages:
[1] splines   stats     graphics  grDevices datasets  grid      utils    
[8] methods   base     

other attached packages:
 [1] foreign_0.8-44     arm_1.4-11         abind_1.3-0        R2WinBUGS_2.1-18  
 [5] coda_0.14-4        lme4_0.999375-39   Matrix_0.999375-50 lattice_0.19-26   
 [9] MASS_7.3-13        xtable_1.5-6       mgcv_1.7-6         ggplot2_0.8.9     
[13] proto_0.3-9.2      reshape_0.8.4      plyr_1.5.2        

loaded via a namespace (and not attached):
[1] nlme_3.1-101  stats4_2.13.0 tools_2.13.0