Hi Philipp, I like to use something like lapply(2:10, function(j) lm.fit(cbind(1, DataMatrix[,j]), DataMatrix[,1]))
for this sort of thing. I'd be curious to know if there are other approaches that are better. --Gray On Wed, Sep 8, 2010 at 4:34 AM, Philipp Kunze <pku...@gwdg.de> wrote: > Hi, > I have huge matrices in which the response variable is in the first > column and the regressors are in the other columns. What I wanted to do > now is something like this: > > #this is just to get an example-matrix > DataMatrix <- rep(1,1000); > Disturbance <- rnorm(900); > DataMatrix[101:1000] <- DataMatrix[101:1000]+Disturbance; > DataMatrix <- matrix(DataMatrix,ncol=10,nrow=100); > > #estimate univariate linear model with each regressor-column, response > in the first column > > for(i in 2:10){ > result <- lm(DataMatrix[,1]~DataMatrix[,i]) > } > > > Is there any way to get rid of the for-loop using mapply (or some other > function)? > > Thanks! > Philipp > > ______________________________________________ > 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. > -- Gray Calhoun Assistant Professor of Economics, Iowa State University http://www.econ.iastate.edu/~gcalhoun/ ______________________________________________ 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.