R help - May 2007 - Estimation of Dispersion parameter in GLM for Gamma Dist.

Hi All,
could someone shed some light on what the difference between the
estimated dispersion parameter that is supplied with the GLM function
and the one that the 'gamma.dispersion( )' function in the MASS
library gives? And is there consensus for which estimated value to
use?


It seems that the dispersion parameter that comes with the summary
command for a GLM with a Gamma dist. is close to (but not exactly):
Pearson Chi-Sq./d.f.

While the dispersion parameter from the MASS library
('gamma.dispersion ( )' ) is close to the approximation given in
McCullagh&Nelder (p.291):
Res.Dev./n*(6+Res.Dev./n) / (6 + 2*Res.Dev./n)

(Since it is only an approximation it seems reasonable that they are
not exactly alike.)


Many thanks,
Fredrik

This is discussed in the book the MASS package (sic) supports, and/or its 
online material (depending on the edition).

On Fri, 25 May 2007, fredrik odegaard wrote:
> Hi All,
> could someone shed some light on what the difference between the
> estimated dispersion parameter that is supplied with the GLM function
> and the one that the 'gamma.dispersion( )' function in the MASS
> library gives? And is there consensus for which estimated value to
> use?
>
>
> It seems that the dispersion parameter that comes with the summary
> command for a GLM with a Gamma dist. is close to (but not exactly):
> Pearson Chi-Sq./d.f.
Sometimes close to, but by no means always.  Again, discussed in MASS.
> While the dispersion parameter from the MASS library
> ('gamma.dispersion ( )' ) is close to the approximation given in
> McCullagh&Nelder (p.291):
> Res.Dev./n*(6+Res.Dev./n) / (6 + 2*Res.Dev./n)
>
> (Since it is only an approximation it seems reasonable that they are
> not exactly alike.)
>
>
> Many thanks,
> Fredrik
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
-- 
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