I'm wondering how to choose an appropriate linear model for a given problem. I have been reading Applied Linear Regression Models by John Neter, Michael H Kutner, William Wasserman and Christopher J. Nachtsheim. I'm still not clear how to choose an appropriate linear model. For multi-factor ANOVA, shall I start with all the interaction terms and do an F-test to see with interaction terms are not significant, then do a linear regression on a model without the non-significant iteration term? Could somebody point me some good book or chapters on this topic?
How to choose appropriate linear model? (ANOVA)
5 messages · Peng Yu, Tal Galili, Rolf Turner +2 more
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On 19/11/2009, at 9:10 AM, Tal Galili wrote:
Hello Peng, What you are talking about is "model selection" process. Although it also sound like you are referring to the more general subject of regression model strategies, consider finding this book: http://www.amazon.com/Regression-Modeling-Strategies-Frank-Harrell/ dp/0387952322 Frank Harrell is a very insightful lecturer, I heard his writing is also good. I would love to read recommendation from other R members regarding your question.
Alan Miller's book ``Subset Selection in Regression'' (Chapman and Hall,
1990) has some relevance.
cheers,
Rolf Turner
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Hi,
On Nov 18, 2009, at 3:33 PM, Rolf Turner wrote:
On 19/11/2009, at 9:10 AM, Tal Galili wrote:
Hello Peng, What you are talking about is "model selection" process. Although it also sound like you are referring to the more general subject of regression model strategies, consider finding this book: http://www.amazon.com/Regression-Modeling-Strategies-Frank-Harrell/dp/0387952322 Frank Harrell is a very insightful lecturer, I heard his writing is also good. I would love to read recommendation from other R members regarding your question.
Alan Miller's book ``Subset Selection in Regression'' (Chapman and Hall, 1990) has some relevance.
You can also look into the "more recent" approaches, like penalized regression. Specifically I'm talking about the lasso or elasticnet. Look for the relevant papers by Trevor Hastie and Tibshirani (you'll get them from their websites) Lucky for you, the "glmnet" package is available for R, implements both the lasso and the elasticnet, and was written by these same people. -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
On Nov 18, 2009, at 4:06 PM, Steve Lianoglou wrote:
Hi, On Nov 18, 2009, at 3:33 PM, Rolf Turner wrote:
On 19/11/2009, at 9:10 AM, Tal Galili wrote:
Hello Peng, What you are talking about is "model selection" process. Although it also sound like you are referring to the more general subject of regression model strategies, consider finding this book: http://www.amazon.com/Regression-Modeling-Strategies-Frank-Harrell/dp/0387952322 Frank Harrell is a very insightful lecturer, I heard his writing is also good. I would love to read recommendation from other R members regarding your question.
Alan Miller's book ``Subset Selection in Regression'' (Chapman and Hall, 1990) has some relevance.
You can also look into the "more recent" approaches, like penalized regression. Specifically I'm talking about the lasso or elasticnet. Look for the relevant papers by Trevor Hastie and Tibshirani (you'll get them from their websites)
Just for the record, Harrell's text cites, discusses and endorses penalized approaches. You can also read his more recent presentation at his website.
David > Lucky for you, the "glmnet" package is available for R, implements > both the lasso and the elasticnet, and was written by these same > people. > > -steve > > -- > Steve Lianoglou > Graduate Student: Computational Systems Biology > | Memorial Sloan-Kettering Cancer Center > | Weill Medical College of Cornell University > Contact Info: http://cbio.mskcc.org/~lianos/contact > > ______________________________________________ > R-help at r-project.org 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.