Generalized linear models (GLMs) also can accommodate outcomes that are not continuous and normally distributed. Indeed, one of the great advantages of GLMs is they provide a unified framework to understand regression models applied to variables assumed to come from a variety of distributions. For this chapter, we will lean heavily on one excellent R package, VGAM, which provides utilities for vector generalized linear models (VGLMs) and vector generalized additive models (VGAMs) [125]. VGLMs and VGAMs are an even more ...
© 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_44. GLM 2
Get Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
