# Assumptions of GAM

I am looking to understand the assumptions of using a generalized additive model.

1) Are the assumptions the same as the assumptions for each equivalent link function in a generalized linear model - e.g. as linear regression, logistic regression etc. along with the assumption that the additive smooth structure is correct and the errors are independent?

2) If these are the same assumptions, do they only play a role when you are making hypothesis tests (e.g. making inference about linear and smooth components)?

ADD: From Simon Wood (author of mgcv package in R), sounds like one indeed treats the need to check assumptions of GAM as the assumptions underlying the generalized linear model with the same link.

• Since a GAM is just a penalized GLM, residual plots should be checked, exactly as for a GLM. I The distribution of scaled residuals should be examined, marginally, and plotted against covariates and fitted values. residuals(model) extracts residuals.
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This might be a bit late but for a GLM, the residuals aren't completely normally distributed (Faraway, 2006). Using the halfnorm (faraway package) function is a good way to detect outliers that are off the trend with noticeable jumps.