Generalized additive models (GAMs) are extensions of the generalized linear models (GLMs) we discussed in earlier chapters. Like GLMs, GAMs accommodate outcomes that are both continuous and discrete. However, unlike GLMs that are fully parametric models, GAMs are semi-parametric models. GAMs allow a mix of a parametric and a nonparametric association between outcome and predictors. For this chapter, we will lean heavily on one excellent R package, VGAM, which provides utilities for vector generalized linear models (VGLMs) ...
© Matt Wiley and Joshua F. Wiley 2019
Matt Wiley and Joshua F. WileyAdvanced R Statistical Programming and Data Modelshttps://doi.org/10.1007/978-1-4842-2872-2_55. GAMs
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