2007/5/24, Lucke, Joseph F <[EMAIL PROTECTED]>:
------------------------------ *From:* 李俊杰 [mailto:[EMAIL PROTECTED] *Sent:* Monday, May 21, 2007 8:12 PM *To:* Lucke, Joseph F *Subject:* Re: [R] How to compare linear models with intercept and those withoutintercept using minimizing adjs R^2 strategy 2007/5/22, Lucke, Joseph F <[EMAIL PROTECTED]>: > > Bottom line: > You use the adjusted R2 with the intercept in your leaps(). If the case > arises that the the intercept-only model (no other predictors) is the result > of you leaps(), then you may test for whether the intercept itself is zero. > > > You cannot compare models with predictors for intercept versus no > intercept as this violates marginality. > I've wrote my question in pdf file. Sorry for my slow understanding. > ------------------------------ > *From:* 李俊杰 [mailto:[EMAIL PROTECTED] > *Sent: *Monday, May 21, 2007 11:34 AM > *To:* Lucke, Joseph F > *Cc:* r-help@stat.math.ethz.ch > *Subject:* Re: [R] How to compare linear models with intercept and those > withoutintercept using minimizing adjs R^2 strategy > > > So when I am using the adjusted R2 and as a penalized optimality > criterion, and I have to compare models with intercept and those without > intercept to decide the final model selected, does my crierion in my > first email make sense? > > Because we know that in leaps(leaps), if we want to select a model by > the adjusted R2 criterion, we have to decide whether the intercept should be > added in advance. But with my adjusted R2 criterion, we don't have to decide > that in advance. > > Thank you so much for your patient clarification. > > > > 2007/5/22, Lucke, Joseph F <[EMAIL PROTECTED]>: > > > > You don't have to embed model selection as hypothesis testing. You > > are using the adjusted R2 and as a penalized optimality criterion. > > > > ------------------------------ > > *From:* 李俊杰 [mailto:[EMAIL PROTECTED] > > *Sent: *Monday, May 21, 2007 10:43 AM > > *To:* Lucke, Joseph F > > *Cc:* r-help@stat.math.ethz.ch > > *Subject:* Re: [R] How to compare linear models with intercept and > > those withoutintercept using minimizing adjs R^2 strategy > > > > > > 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 <[EMAIL PROTECTED]>: > > > > > > I taken the conversation offline and used a pdf file to better > > > display equations. > > > > > > ------------------------------ > > > *From:* 李俊杰 [mailto:[EMAIL PROTECTED] > > > *Sent: *Monday, May 21, 2007 10:14 AM > > > *To:* Lucke, Joseph F > > > *Cc:* r-help@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 <[EMAIL PROTECTED]>: > > > > > > > > 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)$adj.r.squared > > > does in R. I guess what you want me to derive is the formula in that book. > > > > > > Though I know the formula of adjusted R2 for non-central moments, I > > > still want to know whether I am in the right way to compare *linear > > > models with intercept and those without intercept using maximizing adjs R^2 > > > strategy. * > > > ** > > > Actually, I consider the left column consisted of all 1 in > > > predictor matrix Z as the intercept term. Then I apply maximizing > > > adjs R^2 strategy to decide which variables to select. Z is the term in the > > > model: Y=Zb+e. > > > > > > Thanks for your suggestion, and I am looking forward for your reply. > > > > > > > > > > > > -----Original Message----- > > > > From: [EMAIL PROTECTED] > > > > [mailto:[EMAIL PROTECTED] On Behalf Of ??? > > > > Sent: Sunday, May 20, 2007 8:53 PM > > > > To: r-help@stat.math.ethz.ch > > > > Subject: [R] How to compare linear models with intercept and those > > > > > > > > withoutintercept using minimizing adjs R^2 strategy > > > > > > > > Dear R-list, > > > > > > > > I apologize for my many emails but I think I know how to desctribe > > > > my > > > > problem differently and more clearly. > > > > > > > > My question is how to compare linear models with intercept and > > > > those > > > > without intercept using maximizing adjusted R^2 strategy. > > > > > > > > Now I do it like the following: > > > > > > > > > library(leaps) > > > > > n=20 > > > > > x=matrix(rnorm(n*3),ncol=3) > > > > > b=c(1,2,0) > > > > > intercept=1 > > > > > y=x%*%b+rnorm(n,0,1)+intercept > > > > > > > > > > var.selection=leaps(cbind(rep(1,n),x),y,int=F,method="adjr2") > > > > > ##### Choose the model with maximum adjr2 > > > > > var.selection$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, [EMAIL PROTECTED] > > > > Undergranduate in DEP of Tsinghua University, > > > > > > > > [[alternative HTML version deleted]] > > > > > > > > ______________________________________________ > > > > R-help@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, [EMAIL PROTECTED] > > > Undergranduate in DEP of Tsinghua University, > > > > > > > > > > > > -- > > Junjie Li, [EMAIL PROTECTED] > > Undergranduate in DEP of Tsinghua University, > > > > > > -- > Junjie Li, [EMAIL PROTECTED] > Undergranduate in DEP of Tsinghua University, > -- Junjie Li, [EMAIL PROTECTED] Undergranduate in DEP of Tsinghua University,
-- Junjie Li, [EMAIL PROTECTED] Undergranduate in DEP of Tsinghua University,
ZeroIntercept_query2.pdf
Description: Adobe PDF document
______________________________________________ R-help@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.
