Unexpected behavior with weights in binomial glm()

Hi David,

Yes, I agree that the results are "very similar" but I don't
understand why they are not exactly equal given that the data sets are
identical.

And yes, this 1% numerical difference is hugely important to me.  I
have another data set (much larger than this toy example) that works
on the aggregated data (returning a coefficient of about 1) but
returns the warning about perfect separation on the non-aggregated
data (and a coefficient of about 1e15).  So, I'd at least like to be
able to understand where this numerical difference is coming from and,
preferably, a way to tweak my glm() runs (possibly adjusting the
numerical precision somehow???) so that this doesn't happen.

Josh

On Sat, Sep 29, 2012 at 7:50 PM, David Winsemius <dwinsemius at comcast.net> wrote:

On Sep 29, 2012, at 7:10 AM, Josh Browning wrote:

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

Those two results look very similar and it is with a data situation that seems somewhat extreme. The concern is for the 1% numerical  difference in the regression coefficient? Am I reading you correctly?

--
David Winsemius, MD
Alameda, CA, USA