[R] glm with binomial errors - problem with overdispersion

[peter dalgaard]

On Jun 14, 2011, at 08:13 , Prof Brian Ripley wrote:

> I presume you intended 'type' and 'fragment' to be factors (see below).  Such a model would fit exactly.  The additive model
> 
>> model <- glm(y ~ fragment+type, binomial)
> 
> is only modestly over-dispersed, and shows that 'fragment' has zero effect.  Not 'a negligible effect', but no effect.  So something really odd is going on: is this an exercise with artificial data?
> Otherwise you need to explain the exact balance between the two 'fragments' (each fragment has exactly 1/4 success) and your assumption of independent binomial sampling cannot be true.

Also note that success+failure is exactly 102 in fragment 1 and 105 in fragment 2, as is the sum of the successes for each fragment (of course it has to to make exactly 1/4). It is rather easy to suspect that it is actually a 0/1 coding of the type (as in "tick exactly one box"), and not independent binomial data.



-- 
Peter Dalgaard
Center for Statistics, Copenhagen Business School
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Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com