The most straight forward way that I can think of is just: > cor(my.mat)^2 # assuming my.mat is the matrix with your data in the columns
That will give you all the R^2 values for regressing 1 column on 1 column (it is called R-squared for a reason). If you want the R^2 values for regressing one column on all other columns in the matrix, then a short-cut is: > 1-1/diag(solve(cor(my.mat))) Both should be much faster than looping, the 2nd may give problems in trying to invert a very large matrix. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of andy1983 > Sent: Thursday, February 08, 2007 1:29 PM > To: r-help@stat.math.ethz.ch > Subject: [R] loop issues (r.squared) > > > I would like to compare every column in my matrix with every > other column and get the r-squared. I have been using the > following formula and loops: > summary(lm(matrix[,x]~matrix[,y]))$r.squared > where x and y are the looping column numbers > > If I have 100 columns (10,000 iterations), the loops give me > results in a reasonable time. > If I try 10,000 columns, the loops take forever even if there > is no formula inside. I am guessing I can vectorize my code > so that I could eliminate one or both loops. Unfortunately, I > can't figure out how to. > > Any suggestions? > > Thanks. > -- > View this message in context: > http://www.nabble.com/loop-issues-%28r.squared%29-tf3195843.ht > ml#a8873580 > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > ______________________________________________ R-help@stat.math.ethz.ch 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.
