[R] logistic regression based on principle component analysis

SR Millis aa3379 at wayne.edu
Thu Jan 7 18:38:33 CET 2010


Frank Harrell, Jr shows you how to implement this in R, in his book, Regression Modeling Strategies.


~~~~~~~~~~~
Scott R Millis, PhD, ABPP (CN,CL,RP), CStat, CSci
Professor & Director of Research
Dept of Physical Medicine & Rehabilitation
Dept of Emergency Medicine
Wayne State University School of Medicine
261 Mack Blvd
Detroit, MI 48201
Email:  aa3379 at wayne.edu
Email:  srmillis at yahoo.com
Tel: 313-993-8085
Fax: 313-966-7682


--- On Thu, 1/7/10, Kjetil Halvorsen <kjetilbrinchmannhalvorsen at gmail.com> wrote:

> From: Kjetil Halvorsen <kjetilbrinchmannhalvorsen at gmail.com>
> Subject: Re: [R] logistic regression based on principle component analysis
> To: "Steve Lianoglou" <mailinglist.honeypot at gmail.com>
> Cc: r-help at r-project.org, "江文恺" <biology0046 at hotmail.com>
> Date: Thursday, January 7, 2010, 12:27 PM
> for an alternative (lasso) approach,
> look at the packages (CRAN)
> grpreg, grplasso,  glmnet, penalized and certainly
> some others.
> 
> Kjetil B H
> 
> On Thu, Jan 7, 2010 at 2:06 PM, Steve Lianoglou
> <mailinglist.honeypot at gmail.com>
> wrote:
> > Hi,
> >
> > On Thu, Jan 7, 2010 at 11:57 AM, 江文恺 <biology0046 at hotmail.com>
> wrote:
> >>
> >> Dear all:
> >>
> >> I try to analyse a dataset which contain one
> binary response variable and serveral predict variables, but
> multiple colinear problem exists in my dataset, some paper
> suggest that logistic regression for principle components is
> suit for these noise data,
> >> but i only find R can done principle component
> regression using "pls" package,
> >> is there any package that can do the task i need -
> logistic regression based on principle components,
> >> if not, can anyone give some suggestion about how
> to use R to do my work.
> >
> > Is this any different than first doing PCA to do the
> dimensionality
> > reduction (which presumably will take care of your
> colinearity), then
> > doing the logistic regression on your reduced input
> space?
> >
> > If so: no package is really necessary, right? It's
> just a two-step
> > solution you need to write up.
> >
> > -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
> >
> > ______________________________________________
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> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained,
> reproducible code.
> >
> 
> ______________________________________________
> R-help at r-project.org
> mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained,
> reproducible code.
>



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