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. [[alternative HTML version deleted]]