I use gamlss method from library(gamlss) on my full models with interaction terms and try to reduce them with stepGAIC. There are 3 things I want to ask.

  • Do I have to specify a link for the model?
  • stepGAIC give multiple timesthe following message "Model with term cov1:cov2 has failed" and doesn't continues with reducing my full model. What does this error message mean? Does it make sense to continue the calculation
  • In many posts I read that forward/backward method of the stepAIC method is not good-
    are there any "good" alternatives to do that?

Thanks for the answer! I'm very new to GAMLSS Theory. I actually wanted to fit a LM on my continuous variables, but I think that there is heteroscedasticity present, so I switched to GAMLSS to try out other distributions than normal distribution. I'm not really sure whether a link function is needed. Does gamlss this automatically if I specify a distribution from it's selection?

The reason I use stepwise reduction is that I have around 20 other variables to look at and I don't have much time. Strange thing is that if I make forward selection from the Null model with the step function for the LM model (with lm function) I get a model with maineffects and several interactions. Everything is ok. If I do the same procedure with the gamlss and specify the distribution as NO (Normal distribution) my best model is the Null model! Maybe I did something wrong, when I specified the backward selection:

red.model <- stepGAIC(object=object, scope~(cov1+cov2+cov3+cov4+cov5+

Is it normal that stepGAIC is extremely slow in calculating the coefficients? It took 24 hours to calculate 4 steps in stepGAIC.


1 Answer 1


If you don't specify a link for the model, it will default to the mu link being Normal. Is that appropriate for your model?

Not sure what your error message means, but perhaps it has to do with an interaction term being included and the main effect(s) being excluded in the stepwise search. Usually it's a bad idea to exclude main effects when including interactions.

Now-a-days, stepwise methods are frowned upon and shrinkage-based methods encouraged. See also:

Interaction effect in stepwise regression

What are modern, easily used alternatives to stepwise regression?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.