Hi Parthiban,
I urge you to rethink your approach, or at least proceed with extreme
caution. Lower-order terms involved in higher-order interactions may not be
what you think they are. And there are serious problems with stepwise model
selection.

I encourage you to read  a good regression modeling text or consult your
local statistician before proceeding.

-Ista

PS. Yes, the step function has options, which you would know if you had
taken the trouble to read the help file... Try ?step at the R prompt.

On Thu, Apr 1, 2010 at 4:11 PM, Vijaya Parthiban <vijaya.parthiban@gmail.com
> wrote:

> Hello all,
>
> I am trying to do factorial regression using lm() like this (example):
>
> model<-lm(y ~ x1 + x2 + x3 + x4 + x1*x2*x3*x4)
>
> The final term 'x1*x2*x3*x4' adds all possible interactions between
> explanatory variables to the model. i.e. x1:x2, x1:x2:x3, etc, etc. Now,
> the
> issue is that some of the interactions are significant and some are not.
>
> I can manually remove features/interactions using 'update' like this:
>
> model1<-update(model,~. - x1:x2:x4)
>
> .... one by one as long as all the explanatory variables/features or
> interactions are significant. But, this is so tedious. There must be a way
> to say to R automatically  'I want to retain only significant
> features/interactions' OR to do something to update(remove) all
> non-significant interactions.
>
> model2<-step(model)
>
> ..was not very helpful. Are there any options to it?
>
> Can someone shed light on how I can do that? Can glm() or gam() or anything
> else be more powerful to do this? Any help is greatly appreciated!
>
> Many thanks,
> Parthiban.
>
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>
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> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org

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