Hi all,
I have run a ridge regression on a data set 'final' as follows:
reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u,
lambda=seq(0,10,0.01))
Then I enter :
select(reg) and it returns: modified HKB estimator is 19.3409
modified L-W estimator is 36.18617
smallest value of GCV at 10
I think it means that it is advisable to use the results of regression
corresponding to lambda= 10;
so the next thing I do is:
best <- which.min(reg$GCV)
coef(reg)[best,]
which yields:
final$lag1 final$lag2 final$g
final$u
3.147255e-04 1.802505e-01 -4.461005e-02 -1.728046e-09 -5.154932e-04
Now, by changing my data set(final), I repeat the process 100 times and
obtain 100 such vectors which I store as 100 rows in a 100X5 matrix:
matrix[i,]=coef(reg)[best,] (i varying from 1 to 100)
Now my final estimates for the beta's are:
Beta_0=median(matrix[,1])
Beta_1=median(matrix[,2])
Beta_2=median(matrix[,3])
Beta_3=median(matrix[,4])
Beta_4=median(matrix[,5])
I want to find the p-values of each of the estimated beta's.
I am confused how to extract these p values in R (may be we need to go back
to the reg= lm.ridge model corresponding to each final beta estimate, but I
am not sure how to do this through code)
Kindly tell me if any further details are needed.
Thanks for your help.
Regards,
Preetam
--
Preetam Pal
(+91)-9432212774
M-Stat 2nd Year, Room No. N-114
Statistics Division, C.V.Raman
Hall
Indian Statistical Institute, B.H.O.S.
Kolkata.
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