[R] binomial glm warnings revisited
Spencer Graves
spencer.graves at pdf.com
Wed Oct 8 21:57:15 CEST 2003
Thanks, Peter: You are absolutely correct. Thanks again for the
correction. Spencer Graves
Peter Dalgaard BSA wrote:
>Spencer Graves <spencer.graves at pdf.com> writes:
>
>
>
>> This seems to me to be a special case of the general problem of
>>a parameter on a boundary.
>>
>>
>
>Umm, no...
>
>
>
>>>I have this problem with my data. In a GLM, I have 269 zeroes and
>>>only 1 one:
>>>
>>>
>
>I don't think that necessarily gets you a parameter estimate on the
>boundary. Only if the single "1" is smaller or bigger than all the others
>should that happen.
>
>
>
>>>summary(dbh)
>>>Coefficients:
>>> Estimate Std. Error z value Pr(>|z|)
>>>(Intercept) 0.1659 3.8781 0.043 0.966
>>>dbh -0.5872 0.5320 -1.104 0.270
>>>
>>>
>>>
>>>
>>>>drop1(dbh, test = "Chisq")
>>>>
>>>>
>>>>
>>>Single term deletions
>>>Model:
>>>MPext ~ dbh
>>> Df Deviance AIC LRT Pr(Chi) <none> 9.9168
>>>13.9168 dbh 1 13.1931 15.1931 3.2763 0.07029 .
>>>
>>>I now wonder, is the drop1() function output 'reliable'?
>>>
>>>If so, is then the estimates from MASS confint() also 'reliable'? It gives
>>>the same warning.
>>>
>>>
>
>
>
>>>(Intercept) -6.503472 -0.77470556
>>>abund -1.962549 -0.07496205
>>>There were 20 warnings (use warnings() to see them)
>>>
>>>
>
>During profiling, you may be pushing one of the parameter near the
>extremes and get a model where the fitted p's are very close to 0/1.
>That's not necessarily a sign of unreliability -- the procedure is to
>set one parameter to a sequence of fixed values and optimize over the
>other, and it might just be the case that the optimizations have been
>wandering a bit far from the optimum. (I'd actually be more suspicious
>about the fact that the name of the predictor suddenly changed....)
>
>However, if you have only one "1" you are effectively asking whether
>one observation has a different mean than the other 269, and you have
>to consider the sensitivity to the distribution of the predictor. As
>far as I can see, you end up with the test of the null hypothesis
>beta==0 being essentially equivalent to a two sample t test between
>the mean of the "0" group and that of the "1" group, so with only one
>observation in one of the groups, the normal approximation of the test
>hinges quite strongly on a normal distribution of the predictor
>itself.
>
>
>
More information about the R-help
mailing list