[R] Error using glm with poisson family and identity link
Spencer Graves
spencer.graves at pdf.com
Thu Nov 25 20:35:24 CET 2004
Hi, Peter:
What do you do in such situations?
Sundar Dorai-Raj and I have extended "glm" concepts to models
driven by a sum of k independent Poissons, with the a linear model for
log(defectRate[i]) for each source (i = 1:k). To handle convergence
problems, etc., I think we need to use informative Bayes, but we're not
there yet. In any context where things are done more than once [which
covers most human activities], informative Bayes seems sensible.
A related question comes with data representing the differences
between Poisson counts, e.g., with d[i] = X[i]-X[i-1] = the number of
new defects added between steps i-1 and i in a manufacturing process.
Most of the time, d[i] is nonnegative. However, in some cases, it can
be negative, either because of metrology errors in X[i] or because of
defect removal between steps i-1 and i.
Comments?
Best Wishes,
Spencer Graves
Peter Dalgaard wrote:
>Spencer Graves <spencer.graves at pdf.com> writes:
>
>
>
>>Dear Federico: Why do you use the "identity" link? That can
>>produce situations with an average of (-2) Poisson defects per unit,
>>for example. That's physical nonsense.
>>
>>
>
>So is _not_ using the identity link when the model is manifestly
>additive on the identity scale. E.g. calibrating differential
>spectrofluorometry with photon counters recording linear combinations
>of intensities at different wavelengths.
>
>I've bumped into similar situations before (binomial(link=identity), I
>think it was then) and the glm.fit algorithm could use improvement in
>dealing with the parameter constraints in these cases. With the
>standard IRLS algorithm, if the maximum is on the boundary, you
>basically hit a random point on the boundary and get stuck there with
>a search direction pointing out of the valid region.
>
>
>
--
Spencer Graves, PhD, Senior Development Engineer
O: (408)938-4420; mobile: (408)655-4567
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