OK thanks.
In my case I think it might be possible to work around this by reshaping my
data and then using lmlist() to run separate regressions for each data
group. lmlist() is new to me but it looks like it will do the job.
--
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http://r.789695.n4.nabble.com/Linear-
I've done a lot of research on this very topic and found a few solutions. But
all the ways I've discovered involve loops.
Applying it to what you want, the best way I've found is to do (stolen from
an experienced R user, of course):
y<-array(rnorm(100),dim=c(10,10))
x<-array(rnorm(100),dim=c(10,1
I'm new to R and I'm not a Statistician I'm an Accountant, but I'm finding it
an excellent tool for the business analysis work I do.
I need to run LM() where both response and predictor are held in matrices.
The model follows the form:-
regression1 = matrix1.col1 <-> matrix2.col1
regression2 = mat
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