0

I have a GLM model with a Gaussian distribution and a log link and I would like to assess the goodness of fit. I tried to take the regular $R^2$ but that was not possible with my model. I then resorted to the McFadden pseudo $R^2$, which is actually meant for logistic regressions. I would like to know if the McFadden pseudo $R^2$ is usable in my GLM as well? Or if not what would a goodness of fit measure be for GLM models with Gaussian distribution and a log link?

Nick Cox
  • 48,377
  • 8
  • 110
  • 156
KlaasR
  • 1
  • 1
  • 3
    Could you explain why you could not compute an $R^2$? It's certainly possible and might even make some sense. Beware, though, that $R^2$ is often a poor or meaningless measure of goodness of fit, unless you are certain the form of your model is the absolutely correct one. – whuber Jun 05 '19 at 15:45
  • $R^2$ in the sense of the squared correlation between observed and predicted values is certainly computable: nothing makes it "not possible". As always it needs to be viewed circumspectly. The material in https://stats.stackexchange.com/questions/68066/coefficient-of-determination-for-binary-responses is more pertinent than the thread title implies. – Nick Cox Jun 05 '19 at 16:01

0 Answers0