ok,I understand your means, maybe PLS is better for my aim. but I have done
that, also bad. the most questions for me is how to select less variables
from the independent to fit dependent. GA maybe is good way, but I do not
learn it well.
Ben Bolker wrote:
>
> bbslover yeah.net> writes:
>
>>
bbslover yeah.net> writes:
>
[snip]
> the fit result below:
> Call:
> lm(formula = y ~ x1 + x2 + x3, data = pc)
>
> Residuals:
> Min 1Q Median 3Q Max
> -1.29638 -0.47622 0.01059 0.49268 1.69335
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
rm(list=ls())
yx.df<-read.csv("c:/MK-2-72.csv",sep=',',header=T,dec='.')
dim(yx.df)
#get X matrix
y<-yx.df[,1]
x<-yx.df[,2:643]
#conver to matrix
mat<-as.matrix(x)
#get row number
rownum<-nrow(mat)
#remove the constant parameters
mat1<-mat[,apply(mat,2,function(.col)!(all(.col[1]==.col[2:rownum]))
3 matches
Mail list logo