[R] automatic exploration of all possible loglinear models?
d.rizopoulos at erasmusmc.nl
Tue Apr 21 20:51:38 CEST 2009
Have also a look at package bootStepAIC.
Christopher W. Ryan wrote:
> Thank you all. Warnings well-taken.
> My question arose in the context of my "Applied Categorical Data
> Analysis" course. We're mainly using SAS. Recent homework questions have
> been of the form, "The file provides data on 4 variables A, B, C, and D.
> Find a good-fitting model . . . [more questions about interpreting the
> model we come up with] . . . " One of my fellow students asked if SAS
> has some way to automate the process (not the interpretation, but at
> least fitting the many possible models and providing output for us to
> look at.) Apparently SAS does not. I was curious if R did.
> So in this case, it is a "required ritual."
> We've gone over the dubious worth of the P-values in previous class
> Christopher W. Ryan, MD
> SUNY Upstate Medical University Clinical Campus at Binghamton
> 40 Arch Street, Johnson City, NY 13790
> "If you want to build a ship, don't drum up the men to gather wood,
> divide the work and give orders. Instead, teach them to yearn for the
> vast and endless sea." [Antoine de St. Exupery]
> Ben Bolker wrote:
>> Dieter Menne wrote:
>>> Christopher W. Ryan <cryan <at> binghamton.edu> writes:
>>>> Is there a way to automate fitting and assessing loglinear models for
>>>> several nominal variables . . . something akin to step or drop1 or add1
>>>> for linear or logistic regression?
>>> Not strictly for loglinear, but glm works with stepAIC. Make sure that
>>> in the field you are working this approach is an accepted ritual.
>> There is also the package formerly known as "dRedging":
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