For the coefficient to be equal to the correlation, you need to scale y as well.
You can get the correlations by something like the following and then back-calculate the coefficients from there. R> x = matrix(rnorm(100*4e4), 100, 4e4) R> y = rnorm(100) R> rxy = cor(x, cbind(y)) Andy > -----Original Message----- > From: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] On Behalf Of Alex Roy > Sent: Tuesday, July 14, 2009 11:29 AM > To: Vito Muggeo (UniPa) > Cc: r-help@r-project.org > Subject: Re: [R] Linear Regression Problem > > Dear Vito, > Thanks for your comments. But I want to do > Simple linear > regression not Multiple Linear regression. Multiple Linear > regression is not > possible here as number of variables are much more than > samples.( X is ill > condioned, inverse of X^TX does not exist! ) > I just want to take one predictor variable and regress on y and store > regression coefficients, p values and R^2 values. And the > loop go up to > 40,000 predictors. > > Alex > On Tue, Jul 14, 2009 at 5:18 PM, Vito Muggeo (UniPa) > <vito.mug...@unipa.it>wrote: > > > dear Alex, > > I think your problem with a large number of predictors and > a relatively > > small number of subjects may be faced via some > regularization approach > > (ridge or lasso regression..) > > > > hope this helps you, > > vito > > > > Alex Roy ha scritto: > > > >> Dear All, > >> I have a matrix say, X ( 100 X 40,000) > and a vector say, > >> y > >> (100 X 1) . I want to perform linear regression. I have > scaled X matrix > >> by > >> using scale () to get mean zero and s.d 1 . But still I > get very high > >> values of regression coefficients. If I scale X matrix, then the > >> regression > >> coefficients will bahave as a correlation coefficient and > they should not > >> be > >> more than 1. Am I right? I do not whats going wrong. > >> Thanks for your help. > >> Alex > >> > >> > >> *Code:* > >> > >> UniBeta <- sapply(1:dim(X)[2], function(k) > >> + summary(lm(y~X[,k]))$coefficients[2,1]) > >> > >> pval <- sapply(1:dim(X)[2], function(l) > >> + summary(lm(y~X[,l]))$coefficients[2,4]) > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> R-help@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<http://www.r-proje > ct.org/posting-guide.html> > >> and provide commented, minimal, self-contained, reproducible code. > >> > >> > > -- > > ==================================== > > Vito M.R. Muggeo > > Dip.to Sc Statist e Matem `Vianelli' > > Università di Palermo > > viale delle Scienze, edificio 13 > > 90128 Palermo - ITALY > > tel: 091 6626240 > > fax: 091 485726/485612 > > http://dssm.unipa.it/vmuggeo > > ==================================== > > > > [[alternative HTML version deleted]] > > Notice: This e-mail message, together with any attachme...{{dropped:12}} ______________________________________________ R-help@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.
