R help - Mar 2004 - How to fit a classification model in R?

I have about 20 000 cases with discrete variables (some are counts,
some are factors).  I'm interested in fitting a series of models
outcome ~ 1
outcome ~ sex
outcome ~ sex + age
outcome ~ age * sex
outcome ~ age * sex + location
...

I do NOT expect to get any statistical significance out of this at all;
it's purely exploratory (this is a small sample of the full data set).

I'm trying multinom (present continuous because it is not a fast method);
what are recommended ways of doing it?

As part of this question, what would fitting a "Naive Bayes" model
look
like in R?

Perhaps  step(lm.object)  
helps, what use a backward elimination of attributes , 
further i remind in library(leaps)  some helpfuel things
for modellimg selection.

christian


Am Dienstag, 16. M?rz 2004 05:53 schrieb Richard A.
O'Keefe:
> I have about 20 000 cases with discrete variables (some are counts,
> some are factors).  I'm interested in fitting a series of models
> outcome ~ 1
> outcome ~ sex
> outcome ~ sex + age
> outcome ~ age * sex
> outcome ~ age * sex + location
> ...
>
> I do NOT expect to get any statistical significance out of this at all;
> it's purely exploratory (this is a small sample of the full data set).
>
> I'm trying multinom (present continuous because it is not a fast
method);
> what are recommended ways of doing it?
>
> As part of this question, what would fitting a "Naive Bayes"
model look
> like in R?
>
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