Alex Roy <alexroy2...@gmail.com> [Sun, Jun 14, 2009 at 06:43:52AM CEST]:
> Hi Jiim, Thanks . I want to do the following:
>
> 1. each time I need to drop one column, say first column 1 from matrix X.
> 2 then take out row 1 of the remainning matrix and that row becomes
> response (y)
> 3. do lasso regression on remaining X to y.
> 4. store the coefficients
>
> Similarly, in next run
>
> 1. I need to drop 2nd column, from matrix X.
> 2 then take out row 2 of the remainning matrix and that row becomes
> response (y)
> 3. do lasso regression on remaining X ( in example: X2to y.)
> 4. store the coefficients
You may have reasons you want to do this, to me it looks a bit peculiar,
but then I am not too much an expert on penalized regression.
Care to share some thoughts on the theory behind what you are doing?
The following may work (I did not bother to install chemometrics, so untested):
library(lars)
library(chemometrics)
nr <- 50
X <- matrix(rnorm(nr**nr),ncol=nr)
sapply(1:nr, function(i) {
lasso_res=lassoCV(X[i] ~
X[-i],data=data1,K=10,fraction=seq(0.1,1,by=0.1),use.Gram=FALSE)
# to get optimum value of Cross Validation
lasso_coef=lassocoef(X[i] ~
X[-i],data=data1,sopt=lasso_res$sopt,use.Gram=FALSE)})
--
Johannes Hüsing There is something fascinating about science.
One gets such wholesale returns of conjecture
mailto:johan...@huesing.name from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, "Life on the Mississippi")
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