R help - Sep 2012 - Unexpected behavior with weights in binomial glm()

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

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