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.muggeo@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]]
>>
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>> 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.
>>
>>
> --
> ====================================
> 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
> ====================================
>

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