[R] Unexpected behavior with weights in binomial glm()

[Josh Browning]

Hi useRs,

I'm experiencing something quite weird with glm() and weights, and
maybe someone can explain what I'm doing wrong.  I have a dataset
where each row represents a single case, and I run
glm(...,family="binomial") and get my coefficients.  However, some of
my cases have the exact same values for predictor variables, so I
should be able to aggregate up my data frame and run glm(...,
family="binomial",weights=wts) and get the same coefficients (maybe
this is my incorrect assumption, but I can't see why it would be).
Anyways, here's a minimum working example below:

> d = data.frame( RESP=c(rep(1,5),rep(0,5)), INDEP=c(1,1,1,1,0,0,0,0,0,0) )
> glm( RESP ~ INDEP, family="binomial", data=d )

Call:  glm(formula = RESP ~ INDEP, family = "binomial", data = d)

Coefficients:
(Intercept)        INDEP
     -1.609       21.176

Degrees of Freedom: 9 Total (i.e. Null);  8 Residual
Null Deviance:      13.86
Residual Deviance: 5.407        AIC: 9.407
> dAgg = aggregate( d$RESP, by=list(d$RESP, d$INDEP), FUN=length )
> colnames(dAgg) = c("RESP","INDEP","WT")
> glm( RESP ~ INDEP, family="binomial", data=dAgg, weights=WT )

Call:  glm(formula = RESP ~ INDEP, family = "binomial", data = dAgg,
    weights = WT)

Coefficients:
(Intercept)        INDEP
     -1.609       20.975

Degrees of Freedom: 2 Total (i.e. Null);  1 Residual
Null Deviance:      13.86
Residual Deviance: 5.407        AIC: 9.407

Thanks for the help!

Josh