Unlike L1 (lasso) regression or elastic net (mixture of L1 and L2), L2 norm
regression (ridge regression) does not select variables. Selection of
variables would not work properly, and it's unclear why you would want to
omit "apparently" weak variables anyway.
Frank
maths123 wrote
> I have a .txt file containing a dataset with 500 samples. There are 10
> variables.
>
> I am trying to perform variable selection using the ridge regression
> method but I am very confused.
>
> I have input the following:
> diabetes10<-read.table("diabetes10.txt", header=TRUE)
> diabetes10
> library(MASS)
> select(lm.ridge(y=diabetes10 ~ age+sex+bmi+map+tc , diabetes10,
> lambda = seq(0,0.1,0.0001)))
>
> First of all, i am confused about the lamda values,
> Second of all, my output is:
>
> modified HKB estimator is -1.334073e-29
> modified L-W estimator is -5.610557e-28
> smallest value of GCV at 1e-04
>
>
> I have no idea what that is telling me and where I am supposed to work out
> which variables have been selected.
>
> Someone help me out please!
-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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