@inbook{ea1032697b61443fabd62692ffc108c3,
title = "Binomial regression in R",
abstract = "Binomial regression is used to assess the relationship between a binary response variable and other explanatory variables. Popular instances of binomial regression include examination of the etiology of adverse health states using a case-control study and development of prediction algorithms for assessing the risk of adverse health outcomes (e.g., risk of a heart attack). In R, a binomial regression model can be fit using the glm() function. In this chapter, we demonstrate the following aspects of binomial regression, with R code, using real data examples: •To highlight the main components of a binomial model fitting using the glm() function•How to evaluate the modeling assumptions in binomial regression?•How to relax the assumptions when they are violated?•How to fit binomial models for non-independent data?•How to develop and evaluate prediction models for binary response?The chapter is meant to be a quick, practical guide to binomial regression using R. We particularly envision the accompanying task view to be a useful resource on all topics closely related to binomial regression.",
keywords = "Binary response, Diagnostics, GEE, GLMM, Logistic regression, Model assumptions, Prediction",
author = "John Muschelli and Joshua Betz and Ravi Varadhan",
note = "Publisher Copyright: {\textcopyright} 2014 Elsevier B.V.",
year = "2014",
doi = "10.1016/B978-0-444-63431-3.00007-3",
language = "English (US)",
series = "Handbook of Statistics",
publisher = "Elsevier B.V.",
pages = "257--308",
booktitle = "Handbook of Statistics",
}