@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",

year = "2014",

month = jan,

day = "1",

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",

}