Date: Wed, 5 Sep 2007 18:45:34 -0700
Reply-To: David L Cassell <davidlcassell@MSN.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: David L Cassell <davidlcassell@MSN.COM>
Subject: Re: Proc GAM
Content-Type: text/plain; format=flowed
>On 8/24/07, David L Cassell <firstname.lastname@example.org> wrote:
> > woods.steve@GMAIL.COM wrote:
> > >How would I predict future data/values using Proc gam. It says "PRED:
> > >predicted values for each smoothing component and overall predicted
> > >at design points". Is this mean I can predict future data using this
> > >method? I guess, we can use Splue and predict future data using GAM, I
> > >not 100% sure though.
> > >
> > >For example, response is Var1 and my predictor variables are var2, var3
> > >and so on. lets assume, we have data from 2005-2007, I want to see the
> > >prediction values of 2008 of var1?
> > >
> > >You must be curious why I need it! Just to find out stocks! Going to be
> > >rich soon using Proc GAM:)
> > >
> > >Thanks in advance!
> > >
> > >Cheers,
> > >SW
> > Regardless of your subject, I would not use generalized additive models
> > like this.
> > Here's my take on the issue. GAMs are extremely flexible, to the point
> > that they do not make good model builders, and they do not make
> > great parameter estimators. GAMs are excellent for data exploration
> > and structure visualization. GAMS are good for examining the
> > between the dependent variable and the independent variables. But
> > once you have decided on this structure, you might prefer to step back
> > to generalized *linear* models to do the model fit and parameter
> > HTH,
> > David
> > --
> > David L. Cassell
> > mathematical statistician
> > Design Pathways
> > 3115 NW Norwood Pl.
> > Corvallis OR 97330
>Long time no see since sugi.
Yes. It was good to finally get to talk to you in person. Even if I
answer every question you came up with!
>I agree with what you said mostly.
>However, I'd read a paper by Hastie which shows a good use of GAM on
>time series. And personally, I've used GAM to predict daily
>utilization of call centers and it turned out to be good.
>if the purpose of model is prediction, i think it is OK not to worry
>about parameter estimate.
Well, I certainly wouldn't argue with Trevor.
GAM is certainly designed to be used in an exploratory manner, but I tend
to use it otherwise, unless the data are so complex that no simpler model
the intricacies of the data.
And I certainly prefer to use time series methods on time series data, as
as the time series models provide good fits for the data.
David L. Cassell
3115 NW Norwood Pl.
Corvallis OR 97330
A place for moms to take a break!