How to calculate R-square using robust linear regression function

In the linear regression function (regress), one may get the R^2 value directly from one of the 'stats' variable in [b, bint, r, rint, stats] = regress(y,X) function

I want to do a robust linear regression with [b,stats] = robustfit(X,Y)

However, I can't find in the help panel how to assess R^2 from the output variables of the 'robustfit' function.

Any help will be very welcome

JA

Answer 1

[Matt Fig]

[brob, rob_stats] = robustfit(x,y);
rsquare = corr(y,brob(1)+brob(2)*x)^2

Answer 2

[Christian]

Hi there, although this thread is really old, I'm gonna give it a shot :-) I encountered the same problem and Matt's formula seems to solve it beautifully when regressing against only 1 independent variable. However, my x contains 4 factors. How would you go about calculating R-squared in that case? I tried the following, but it doesn't seem to work:

 B_Rsqrd(1,j) = corr(Y,b(1)+b(2)*X(:,1)+b(3)*X(:,2)+b(4)*X(:,3)+b(5)*X(:,4))^2;

Can someone help me out here?

Any help is much appreciated!

Christian