[R] Error using glm with poisson family and identity link

[Spencer Graves]

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