[R] Regression using mapply?

[Greg Snow]

This really depends on why you want to do this and what results you want.  If your main goal is to look at some basic tests, goodness of fit, then the add1 function may do everything you need.  If you just want coefficient estimates then some basic matrix algebra will give those to you.

Another option would be to reshape the data to long format and use lmList from the nlme package (the above will be quicker if you do not need everything that lm gives you).

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Philipp Kunze
> Sent: Wednesday, September 08, 2010 5:35 AM
> To: R-help at r-project.org
> Subject: [R] Regression using mapply?
> 
> 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
> 
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