R help - Jun 2007 - glm() for log link and Weibull family

I need to be able to run a generalized linear model with a log() link 
and a Weibull family, or something similar to deal with an extreme 
value distribution.

I actually have a large dataset where this is apparently necessary. 
It has to do with recovery of forensic samples from surfaces, where 
as much powder as possible is collected. This apparently causes the 
results to conform to some type of extreme value distribution, so 
Weibull is a reasonable starting point for exploration.

I have tried ('surface' and 'team' are factors)

glm(surfcount ~ surface*team, data=powderd, family=Gamma(link='log'))

but this doesn't quite do the trick. The standardized deviance 
residuals are still curved away from normal at the tails.

Thanks for any info you can give on this nonstandard model.
===============================================================Robert A.
LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"

On Fri, 8 Jun 2007, Robert A. LaBudde wrote:
> I need to be able to run a generalized linear model with a log() link
> and a Weibull family, or something similar to deal with an extreme
> value distribution.
The Weibull with log link is not a GLM, but survreg() in package survival
can fit it, as well as other extreme-value distributions.
> I actually have a large dataset where this is apparently necessary.
> It has to do with recovery of forensic samples from surfaces, where
> as much powder as possible is collected. This apparently causes the
> results to conform to some type of extreme value distribution, so
> Weibull is a reasonable starting point for exploration.
>
> I have tried ('surface' and 'team' are factors)
>
> glm(surfcount ~ surface*team, data=powderd,
family=Gamma(link='log'))
>
> but this doesn't quite do the trick. The standardized deviance
> residuals are still curved away from normal at the tails.
>
> Thanks for any info you can give on this nonstandard model.
It's perfectly standard, just not a GLM.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595