I realize this may be a potentially broad question, but I was wondering whether there are generalizable assumptions that indicate the use of a GAM (Generalized additive model) over a GLM (Generalized linear model)?
Someone recently told me that GAMs should only be used when I assume the data structure to be "additive", i.e. I expect additions of x to predict y. Another person pointed out that a GAM does a different type of regression analysis than a GLM, and that a GLM is preferred when linearity can be assumed.
In the past I have been using a GAM for ecological data, e.g.:
- continuous timeseries
- when the data did not have a linear shape
- I had multiple x to predict my y that I thought to have some nonlinear interaction that I could visualize using "surface plots" together with a statistical test
I obviously don't have a great understanding of what a GAM does different than a GLM. I believe it's a valid statistical test, (and I see an increase in the use GAMs, at least in ecological journals), but I need to know better when it's use is indicated over other regression analyses.