[R] model selection with step function

[Antonio Olinto]

Dear Ehlers, thanks for your message.

Following the example on stepAIC and Venebles & Ripley’s book, it seems that
update rearranges the terms. I didn’t understand how to indicate the formula in
the function.

I have the initial model U ~ var1+var2+var3+var4 (family Gaussian). I want first
to select the terms, putting main effects first. If I just write step(model) it
will take out no significant variables (lets say var2) and will give U ~
var1+var3+var4. Supposing the var4 have the “strongest” effect upon U, followed
by var1 and var3, I would like to have the out put U ~ var4+var1+var2.

Is it possible to do so?

Thanks again.

Antonio Olinto
Biologist
Sao Paulo Fisheries Institute

PS. Unfortunately I don’t have Regression Modeling Strategies around here


Citando P Ehlers <ehlers at math.ucalgary.ca>:

> 
> Antonio Olinto wrote:
> 
> > Hello,
> > 
> > I have a doubt in using the function step (step wise) to select glm
> models.
> > 
> > Usually I apply the gamma distribution to analyze fishery data. To select
> the
> > terms I use a routine where I first compare single term models to the null
> model
> > (eg. U~1 vs. U~depth; U~1 vs. U~latitude; etc. – where U= abundance) and,
> by
> > means of the result given by a likelihook function applied for each
> comparison,
> > I select the “strongest” effect, let’s say depth. Then I run a new step
> > comparing the U~depth vs. U~depth+latitude; U~depth vs. U~depth+... etc.
> Making
> > this way I put the terms in “magnitude” order.
> > 
> > I tried to make a gaussian model using the step(glm.model) function to
> select
> > the terms but I saw that in the output table given by anova(glm.model) the
> > selected terms kept the original order.
> > 
> > Is it possible to have the terms in the model rearranged, as in my
> example?
> > 
> > Thanks for any help. I read Chambers and Hastie’s “Statistical Models in
> S”,
> > Venables and Ripley “Modern Applied Statistics” and, of course, R help but
> I
> > couldn’t get the trick.
> > 
> 
> I don't know if this will get you there, but
> 
> 1. I would use stepAIC in package MASS;
> 2. set argument trace = TRUE;
> 3. think very hard about the interpretation of the model;
> 4. read also Frank Harrell's "Regression Modeling Strategies".
> 
> Peter
> 
> 
> > Antonio
> > 
> > --
> > Biologist
> > Sao Paulo Fisheries Institute
> > 
> > 
> > 
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> -- 
> Peter Ehlers
> Department of Mathematics and Statistics
> University of Calgary, 2500 University Dr. NW       ph: 403-220-3936
> Calgary, Alberta  T2N 1N4, CANADA                  fax: 403-282-5150
> 
> 
> 



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