From: 李俊杰 <klijunjie_at_gmail.com>

Date: Mon, 21 May 2007 23:42:33 +0800

Date: Mon, 21 May 2007 23:42:33 +0800

I have a question about what you've wrote in your pdf file. Why must we view my problem in the viewpoint of hypothesis testing? Is testing the original philosophy of maximizing Fisher's A-statistic to choose a optimum model?

Thanks.

2007/5/21, Lucke, Joseph F <Joseph.F.Lucke_at_uth.tmc.edu>:

*>
**> I taken the conversation offline and used a pdf file to better display
**> equations.
**>
**> ------------------------------
**> *From:* 李俊杰 [mailto:klijunjie_at_gmail.com]
**> *Sent:* Monday, May 21, 2007 10:14 AM
**> *To:* Lucke, Joseph F
**> *Cc:* r-help_at_stat.math.ethz.ch
**> *Subject:* Re: [R] How to compare linear models with intercept and those
**> withoutintercept using minimizing adjs R^2 strategy
**>
**>
**>
**>
*

> 2007/5/21, Lucke, Joseph F <Joseph.F.Lucke@uth.tmc.edu>:

*> >
**> > One issue is whether you want your estimators to be based on central
**> > moments (covariances) or on non-central moments. Removing the intercept
**> >
**> > changes the statistics from central to non-central moments. The
**> > adjusted R2, by which I think you mean Fisher's adjusted R2, is based on
**> > central moments (ratio of unbiased estimators of variances---central
**> > moments). So if you remove the intercept, you must re-derive the
**> > adjusted R2 for non-central moments --- you can't just plug in the
**> > number of independent variables as zero.
**>
**>
**> I have consulted A.J. Miller's Subset Selection in Regression(1990), and I
**> found what I was talking about adjusted R^2 was exactly as you
**> said--Fisher's A-statisitc. The formula of adjusted R^2 without the
**> intercept in that book was also the same as what summary(lm)$adj.r.squared
**> does in R. I guess what you want me to derive is the formula in that book.
**>
**> Though I know the formula of adjusted R2 for non-central moments, I still
**> want to know whether I am in the right way to compare *linear models with
**> intercept and those without intercept using maximizing adjs R^2 strategy.
**> *
**> **
**> Actually, I consider the left column consisted of all 1 in predictor
**> matrix Z as the intercept term. Then I apply maximizing adjs R^2 strategy
**> to decide which variables to select. Z is the term in the model: Y=Zb+e.
**>
**> Thanks for your suggestion, and I am looking forward for your reply.
**>
**>
**>
**> -----Original Message-----
**> > From: r-help-bounces_at_stat.math.ethz.ch
**> > [mailto:r-help-bounces_at_stat.math.ethz.ch] On Behalf Of ???
**> > Sent: Sunday, May 20, 2007 8:53 PM
**> > To: r-help_at_stat.math.ethz.ch
**> > Subject: [R] How to compare linear models with intercept and those
**> > withoutintercept using minimizing adjs R^2 strategy
**> >
**> > Dear R-list,
**> >
**> > I apologize for my many emails but I think I know how to desctribe my
**> > problem differently and more clearly.
**> >
**> > My question is how to compare linear models with intercept and those
**> > without intercept using maximizing adjusted R^2 strategy.
**> >
**> > Now I do it like the following:
**> >
**> > > library(leaps)
**> > > n=20
**> > > x=matrix(rnorm(n*3),ncol=3)
**> > > b=c(1,2,0)
**> > > intercept=1
**> > > y=x%*%b+rnorm(n,0,1)+intercept
**> > >
**> > > var.selection=leaps(cbind(rep(1,n),x),y,int=F,method="adjr2")
**> > > ##### Choose the model with maximum adjr2
**> > > var.selection$which[var.selection$adjr2==max(var.selection$adjr2),]
**> > 1 2 3 4
**> > TRUE TRUE TRUE FALSE
**> >
**> >
**> > Actually, I use the definition of R-square in which the model is without
**> >
**> > a intercept term.
**> >
**> > Is what I am doing is correct?
**> >
**> > Thanks for any suggestion or correction.
**> > --
**> > Junjie Li, klijunjie_at_gmail.com
**> > Undergranduate in DEP of Tsinghua University,
**> >
**> > [[alternative HTML version deleted]]
**> >
**> > ______________________________________________
**> > R-help_at_stat.math.ethz.ch mailing list
**> > https://stat.ethz.ch/mailman/listinfo/r-help
**> > PLEASE do read the posting guide
**> > http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
**> > and provide commented, minimal, self-contained, reproducible code.
**> >
**>
**>
**>
**> --
**> Junjie Li, klijunjie_at_gmail.com
**> Undergranduate in DEP of Tsinghua University,
**>
**>
*

-- Junjie Li, klijunjie_at_gmail.com Undergranduate in DEP of Tsinghua University, [[alternative HTML version deleted]]Received on Mon 21 May 2007 - 15:59:35 GMT______________________________________________ R-help_at_stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Mon 21 May 2007 - 16:31:00 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*