Re: [R] step.gam and number of tested smooth functions

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Wed 12 Oct 2005 - 19:20:42 EST

step.gam is a tricky function to use correctly. You will need to consult the original documentation (in Chambers & Hastie ca 1992) or ask the package author for help.

BTW, it uses loess not lowess.

On Wed, 12 Oct 2005 astrzelczak@ps.pl wrote:

>
> Hi,
>
> I'm working with step.gam in gam package. I'm interested both in spline and
> lowess functions and when I define all the models that I'm interested in I get
> something like that:
>
>> gam.object.ALC<-gam(X143S~ALC,data=dane,family=binomial)
>>
> step.gam.ALC<-step.gam(gam.object.ALC,scope=list("ALC"=~1+ALC+s(ALC,2)+s(ALC,3)+s(ALC,4)+s(ALC,6)+s(ALC,8)+lo(ALC,degree=1,span=.5)+lo(ALC,degree=2,span=.5)+lo(ALC,degree=1,span=.25)+lo(ALC,degree=2,span=.25)))
> Start: X143S ~ ALC; AIC= 104.0815
> Trial: X143S ~ 1; AIC= 111.1054
> Trial: X143S ~ s(ALC, 2); AIC= 103.3325
> Step : X143S ~ s(ALC, 2) ; AIC= 103.3325
>
> Trial: X143S ~ s(ALC, 3); AIC= 102.9598
> Step : X143S ~ s(ALC, 3) ; AIC= 102.9598
>
> Trial: X143S ~ s(ALC, 4); AIC= 102.2103
> Step : X143S ~ s(ALC, 4) ; AIC= 102.2103
>
> Trial: X143S ~ s(ALC, 6); AIC= 102.4548
>
> I have impression that the algorithm stops when the next trial gives higher AIC
> without examining further functions. When I deleted some of the spline functions
> that were worse than s(ALC,4) I got:
>
> >
> step.gam.ALC<-step.gam(gam.object.ALC,scope=list("ALC"=~1+ALC++s(ALC,4)+lo(ALC,degree=1,span=.5)+lo(ALC,degree=2,span=.5)+lo(ALC,degree=1,span=.25)+lo(ALC,degree=2,span=.25)))
> Start: X143S ~ ALC; AIC= 104.0815
> Trial: X143S ~ 1; AIC= 111.1054
> Trial: X143S ~ s(ALC, 4); AIC= 102.2103
> Step : X143S ~ s(ALC, 4) ; AIC= 102.2103
>
> Trial: X143S ~ lo(ALC, degree = 1, span = 0.5); AIC= 99.8127
> Step : X143S ~ lo(ALC, degree = 1, span = 0.5) ; AIC= 99.8127
>
> Trial: X143S ~ lo(ALC, degree = 2, span = 0.5); AIC= 100.5275
>
> Lowess turned out to be better in this situation. Is there any way to examine
> all the models without stopping when AIC is higher in the next trial? Or maybe
> manual handling is the only solution?
>
> thanks for help in advance
>
> Agnieszka Strzelczak
>
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-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Received on Wed Oct 12 19:33:26 2005

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