Multiple linear regression with for loop
I'm not sure if this is what you want but if you have a matrix as response, you can use the matrix ~ term: example: x <- 1:10 y <- rep(rnorm(10,x,0.5),10) dim(y) <- c(10,10) y <- as.matrix(y) coef(lm(y~x)) Bart
Markus "M?hlbacher" wrote:
Hi everyone! I have an array containing the following fields for over hundred compounds: cpd, activity, fixterm, energy1, energy2, energy3, ... I want to run a multiple linear regression on all entries of an array. Therefore I tried to do this with a for loop. (Maybe there is a direct way of calculating it using apply, but I don't know that either.) Actually i tried the following code: ...
> attach(data)
> for(i in 1:length(cpd)) {
> fitted.model <- lm(activity ~ fixterm + i)
> coef(fitted.model)
> }
...
Unfortunatly this loop doesn't give the intended correlation coefficients
of each regression. If I insert a line "print(i)" into the loop the
desired values for i are printed correctly. Only the coefficient outputs
are missing.
Probably the solution is very near, but I just can't see it.
Many thanks in advance,
Markus
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