I started learning ridge regression in R. I applied the linear ridge regression to my full data set and got the following results.
gridge<-lm.ridge(divorce ~., data=divusa, lambda=seq(0,35,0.02))
select(gridge)
modified HKB estimator is 0.07693804
modified L-W estimator is 0.3088377
smallest value of GCV at 0.02 which.min(gridge$GCV)
0.02
2
round(coef(gridge)[2,-1],3)
year unemployed femlab marriage birth military
-0.195 -0.053 0.790 0.148 -0.118 -0.042
round(coef(g)[-1],3)
year unemployed femlab marriage birth military
-0.203 -0.049 0.808 0.150 -0.117 -0.043
Questions:
- How do I interpret the results?
- Do I have to do anything else for interpretation?
Best Answer
Some things to look at when fitting the ridge regression
regression coefficients for this fit:
ordinary least square fit:
The ridge regression centers and scales the predictors so you need to do the same when calculating the fit. You can add back the mean of the response.
more info on ridge regression: http://tamino.wordpress.com/2011/02/12/ridge-regression/