On Wed, 19 Apr 2006, Smith, Phil wrote:
> Hi R-people:
>
> When I use the command to fit a model with an intercept, only:
>
> glm ( formula=haspdata ~ 1, data=dat, family=binomial, weights=
> dat$hy.wgt.s, subset=(dat$haspdat0!=3) )
>
> I get the message:
>
> Warning message:
> non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)
The weights in a binomial glm are binomial denominators, and glm() is
pointing out that when it multiplies the supplied outcome variable by the
weight it doesn't get an integer.
You can get around this by using family=quasibinomial(), but this still
assumes that the weights are precision weights. If they are probability
weights you need to use either survey::svyglm or something like
Design::robcov.
-thomas
>
> Does anyone know what this means?? The data for this command is listed
> below.
>
> Thanks,
> Phil Smith
> CDC
>
> dat.hy.wgts.s
> Here is my data:
>> table( dat$haspdata )
> 0 1
> 21890 9097
>> is.integer( dat$haspdata )
> [1] TRUE
>
> dat$hy.wgt.s
>> summary( dat$hy.wgt.s)
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> 0.003374 0.657700 0.874200 1.000000 1.204000 8.557000
>
>
> dat$haspdat0
>> table(dat$haspdat0)
> 1 2 3
> 21890 8989 108
>
>> is.integer( dat$haspdat0 )
> [1] TRUE
>
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>
Thomas Lumley Assoc. Professor, Biostatistics
[EMAIL PROTECTED] University of Washington, Seattle
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