I am not a statistician and would be very thankful if someone could clarify me this.
I am reading about the Generalized Additive Models that 'they don't handle interaction well. Rather than fitting multiple variables simultaneously, the algorithm fits a smooth curve to each variable and then combines the results additively, thus giving rise to the name Generalized Additive Models.' (in http://ecology.msu.montana.edu/labdsv/R/labs/lab5/lab5.html)
Could someone give an example of two interacting variables and how they are being handled by GAMs?
Hello,
I am not sure about the "don't handle interaction well" part. I believe that there are actually some structures available in R (mgcv package) that take into account the interaction. For instance, you can use the tensor product (which by definition is a bivariate function) with te(). A very simple example for a model with interaction y ~ f(x0, x1) where y = x02 + x13 would be
library(mgcv)
# simulate data: y depends explicitly on x0 and x1
x0
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