R help - Oct 2010 - best predictive model for mixed catagorical/continuous variables

Would anybody be able to advise on which package would offer the best
approach for producing a model able to predict the probability of species
occupation based upon a range of variables, some of them catagorical (eg.
ten soil types where the numbers assigned are not related to any
qualitative/quantitative continuum or vegetation type) and others continuous
such as field size or vegetation height.

I have tried using the TREE package but the models produced seem too
simplistic and discard most variables with the result that there is no
predictive power in the result.

I would expect that there will be interactions between variables eg. if the
vegetation is grassland then the vegetation height variable will mediate the
interaction, if the vegetation is arable then crop type will be more
significant.

Would it be possible to use GLM or GAM models for this type of predictive
modelling?

Any assistance would be greatly appreciated - it's several years since I
last used R for this type of work and unfortunately I don't have the support
network of a university to turn to for advice these days!
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> I would expect that there will be interactions between variables eg. if 
> the
> vegetation is grassland then the vegetation height variable will mediate 
> the
> interaction, if the vegetation is arable then crop type will be more
> significant.
>
> Would it be possible to use GLM or GAM models for this type of predictive
> modelling?
The answer is probably.  I would recommend getting a copy of:
Mixed Effects Models and Extensions in Ecology with R (Statistics for 
Biology and Health) by Alain F. Zuur, Elena N. Ieno, Neil Walker, and 
Anatoly A. Saveliev Springer, 2009)

It covers things very nicely and gives specific R code.

Rob