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
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