You can get the ols coefficients with basic matrix operations as well (
https://files.nyu.edu/mrg217/public/ols_matrix.pdf) and by that avoid one
of the loops. I do not know how efficient this is but I have attached an
example you can paste bellow your code. Here, one x-array is used as a
right hand side variable for all y-arrays in each loop.

The coefficients match, but they are in different order.


#----------------------------------------------------------------------------------------------------------------------------

#Original code here....


m1=array(1,nrow(x))

#Creates an array of ones

MISC2<-0


for(j in 1:ncol(x)){

mX=cbind(m1,x[,j])

reg2<-solve(t(mX)%*%mX)%*%t(mX)%*%y

MISC2<-cbind(MISC2,reg2) #for coefficients

}


coef<-matrix(MISC2[,-1],ncol=50)


coef[,1];coef(lm(y[,1]~x[,1])) #test passed

ncol(coef)

#as desired, 50 regressions.



MISC1

MISC2


#-------------------------------------------------------------------------------------------------------------------------------------

2011/12/26 iliketurtles <isaacm200@gmail.com>

> Hi, I'm quite new to R (1 month full time use so far). I have to run loop
> regressions VERY often in my work, so I would appreciate some new
> methodology that I'm not considering.
>
>
> #---------------------------------------------------------------------------------------------
> y<-matrix(rnorm(100),ncol=10,nrow=10)
> x<-matrix(rnorm(50),ncol=5,nrow=10)
>
> #Suppose I want to run the specification y=A+Bx+error, for each and every
> y[,n] onto each and every x[,n].
> #So with:
> ncol(y);ncol(x)
> #I should end up with 10*5=50 regressions in total.
>
> #I know how to do this fine:
> MISC1<-0
> for(i in 1:ncol(y)){
> for(j in 1:ncol(x)){
> reg<-lm(y[,i]~x[,j])
> MISC1<-cbind(MISC1,coef(reg)) #for coefficients
> }}
> coef<-matrix(MISC1[,-1],ncol=50)
>
> coef[,1];coef(lm(y[,1]~x[,1])) #test passed
> ncol(coef)                                #as desired, 50 regressions.
>
> #---------------------------------------------------------------------------------------------
>
> Now for my question: Is there easier or better methods of doing this? I
> know
> of a lapply method, but the only lapply way I know of for lm(..) is
> basically doing a lapply inside of a lapply, meaning it's exactly the same
> as the double loop above... I'm looking to escape from loops.
>
> Also, if any of you could share your top R tips that you've learned over
> the
> years, I'd really appreciate it. Tiny things like learning that array() and
> matrix() can have a 3rd dimension, learning of strsplit, etc.. have helped
> me immeasurably. (Not that I'm also googling for this stuff! I'm doing R 14
> hours a day!).
>
> Thanks.
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Other-ways-to-lm-regression-non-loop-tp4234487p4234487.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> 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.
>

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