[R] Extreme AIC or BIC values in glm(), logistic regression
Gavin Simpson
gavin.simpson at ucl.ac.uk
Fri Mar 20 09:44:09 CET 2009
On Fri, 2009-03-20 at 12:39 +1100, Gad Abraham wrote:
> Maggie Wang wrote:
> > Hi, Dieter, Gad, and all,
> >
> > Thank you very much for your reply!
> >
> > So here is my data, you can copy it into a file names "sample.txt"
>
> Hi Maggie,
>
> With this data (allowing for more iterations) I get:
>
> > lr <- glm(fo, family=binomial(link=logit), data=matrix,
> control=glm.control(maxit=100))
> Warning message:
> In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
> etastart, :
> fitted probabilities numerically 0 or 1 occurred
>
> which indicates, as Thomas has said, perfect separation, which occurs
> because you're trying to fit too many variables with not enough data.
It is worth mentioning that, in and of itself, that warning does not
necessarily indicate a separation issue - something I was unsure about
recently. You can get that warning (and I did for several data sets in a
recent problem I enquired on the list about) where the fitted values
really do become numerically 0 or 1 without separation.
For example, see this response to my original question on the list:
http://article.gmane.org/gmane.comp.lang.r.general/134472/
There Ioannis Kosmidis presents a number of ways to investigate the
results of a logit model fit for such issues.
G
>
> Cheers,
> Gad
>
--
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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