# Robust regression goodness of fit

I'm using R to compute robust multiple linear regression. I use the command rlm from the package MASS.

As psi function I use psi.huber or psi.bisquare.

Is there a way to get an estimator of the goodness of fit of the model? Maybe something comparable to the Adjusted R-squared, for the parametric multiple linear regression?

Moreover, am I right saying that this kind of robust regression doesn't solve the problem of non-normality of the dependent or independent variables?

• you should use the more modern implementations, in the robustbase package. That one will return a more complete output. For the two other questions, what do you mean by " problem of non-normality of the dependent or independent variables"? – user603 Mar 26 '14 at 20:29
• I was thinking to take the last question away, but since you ask. I am concerned by the fact that normality test are failing when I apply them to dependent and independent variables. Can you link a guide to how to use the robustbase package? – Forinstance Mar 26 '14 at 20:58
• And, when I use lmrob (robustbase) I obtain different results, even if I get an R-sqaured, from those that I obtain with rlm. Is there a way to use psi.huber or psi.bisquare with lmrob? – Forinstance Mar 26 '14 at 23:08
• Concerning your first comment, I direct you to this discussion. The question in your second comment is now a pure programming question that should perhaps be moved to stackoverflow. Please update your question to specify what is being asked. – user603 Mar 27 '14 at 9:17