Message-ID: <bbdc7ed01001070906j2c0ad3cck2427af9c18916e3c@mail.gmail.com>
Date: 2010-01-07T17:06:45Z
From: Steve Lianoglou
Subject: logistic regression based on principle component analysis
In-Reply-To: <BLU139-W9DAD76ECAF36E5D27F850B4710@phx.gbl>
Hi,
On Thu, Jan 7, 2010 at 11:57 AM, ??? <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