4. GLM 2

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 flexible class of models where there may be multiple responses. However, beyond offering flexibility of multiple parameters, the VGAM package implements over 20 link functions, well over 50 different models/assumed distributions. We will only scratch the surface of the VGAM package capabilities in this chapter, but its great flexibility means that we will not need to introduce many different packages nor many different functions. If you would like to learn about VGLMs and VGAMs in far greater depth, we recommend an excellent book by the author of the VGAM package [125].

                library(checkpoint)
                checkpoint("2018-09-28", R.version = "3.5.1",
                  project = book_directory,
                  checkpointLocation = checkpoint_directory,
                  scanForPackages = FALSE,
                  scan.rnw.with.knitr = TRUE, use.knitr = TRUE)
               
                library(knitr)
                library(data.table)
                library(ggplot2)
                library(ggthemes)
                library(scales)
                library(viridis)
                library(VGAM)
                library(ipw)
                library(JWileymisc)
                library(xtable)
                library(texreg)
               
                options(
                  width = 70,
                  stringsAsFactors = FALSE,
                  datatable.print.nrows = 20,
                  datatable.print.topn = 3,
                  digits = 2)