“ * ” vs “ : ” in R for modelling [closed]

问题

Closed. This question needs to be more focused. It is not currently accepting answers.

---

Want to improve this question? Update the question so it focuses on one problem only by editing this post.

Closed 3 years ago.

I'm sure this question must have been asked but I have no luck finding it in the archives. Anyways, I always thought that " : " or " * " meant the same thing when adding interaction terms. For example:

amount of gas ~ temperature * gas type amount of gas ~ temperature:gas type

However, now that I've started using GLMs I see that these generate different AIC scores, different estimates etc. when I switch between the two. Can anyone explain to me why this happens? Is it a problem with the stats package in R?

Thank you!

回答1:

From help(formula):

 In addition to ‘+’ and ‘:’, a number of other operators are useful
 in model formulae.  The ‘*’ operator denotes factor crossing:
 ‘a*b’ interpreted as ‘a+b+a:b’.

来源：https://stackoverflow.com/questions/40567421/vs-in-r-for-modelling