[R] Effect size in GLIM models

[Behnke Jerzy]

Dear All,
I wonder if anyone can advise me as to whether there is a consensus as
to how the effect size should be calculated from GLIM models in R for
any specified significant main effect or interaction.

In investigating the causes of variation in infection in wild animals,
we have fitted 4-way GLIM models in R with negative binomial errors.
These are then simplified using the STEP procedure, and finally each of
the remaining terms is deleted in turn, and the model without that term
compared to a model with that term to estimate probability

An ANOVA of each model gives the deviance explained by each interaction
and main effect, and the percentage deviance attributable to each factor
can be calculated from NULL deviance.

However, we estimate probabilities by subsequent deletion of terms, and
this gives the LR statistic. Expressing the value of the LR statistic as
a percentage of 2xlog-like in a model without any factors, gives lower
values than the former procedure.

Are either of these appropriate? If so which is best, or alternatively
how can % deviance be calculated. We require % deviance explained by
each factor or interaction,  because we need to compare individual
factors (say host age) across a range of infections.

Any advice will be most gratefully appreciated. I can send you a worked
example if you require more information.

Jerzy. M. Behnke,
The School of Biology,
The University of Nottingham,
University Park,
NOTTINGHAM, NG7 2RD

Tel: +0044 (0) 115 951 3208
Fax: +0044 (0) 115 951 3251

http://www.nottingham.ac.uk/biology/contact/academics/behnke/overview.ph
tml?P=1&R=1&S=&ID=11&from=iai&m1=&m2=
Useful links to field stations:
http://www.quintastudies.info/index.htm



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