Binomial regression in R

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)257-308
Number of pages52
JournalHandbook of Statistics
Volume32
DOIs
StatePublished - 2014

Fingerprint

Regression
Health
Binomial Model
Binary Response
Case-control Study
Evaluate
Model Fitting
Prediction Model
Statistical Models
Regression Model
Attack
Resources
Prediction
Modeling
Demonstrate

Keywords

  • Binary response
  • Diagnostics
  • GEE
  • GLMM
  • Logistic regression
  • Model assumptions
  • Prediction

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Cite this

Binomial regression in R. / Muschelli, John; Betz, Joshua Francis; Varadhan, Ravi.

In: Handbook of Statistics, Vol. 32, 2014, p. 257-308.

Research output: Contribution to journalArticle

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