General Additive models, similar to GLMs and GLMMs but it mixes general linear models with additive models. Used for non-linear relationships.
They are good for maximizing the prediction of a dependant variable y from various distributions by estimating unspecific, non-parametric functions of the predictor variables.
How is it different from GLMs?
1. distribution of the dependant variable can be non-normal.
2. The dependant variable values are predicated from a linear combination of predictor variables.
Common Problems.
1. Results are not as easy to interpret as GLM and GLMMs
2. Very easy to “over fit” the data and not be able to replicate on future trials
3. Harder to summary and communicate to others
Heres a good website that explains GAMs well : http://www.statsoft.com/textbook/generalized-additive-models#additive%20models