### 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 language | English (US) |
---|---|

Pages (from-to) | 257-308 |

Number of pages | 52 |

Journal | Handbook of Statistics |

Volume | 32 |

DOIs | |

State | Published - 2014 |

### Fingerprint

### 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.

Research output: Contribution to journal › Article

*Handbook of Statistics*, vol. 32, pp. 257-308. https://doi.org/10.1016/B978-0-444-63431-3.00007-3

}

TY - JOUR

T1 - Binomial regression in R

AU - Muschelli, John

AU - Betz, Joshua Francis

AU - Varadhan, Ravi

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Binary response

KW - Diagnostics

KW - GEE

KW - GLMM

KW - Logistic regression

KW - Model assumptions

KW - Prediction

UR - http://www.scopus.com/inward/record.url?scp=84918549545&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84918549545&partnerID=8YFLogxK

U2 - 10.1016/B978-0-444-63431-3.00007-3

DO - 10.1016/B978-0-444-63431-3.00007-3

M3 - Article

AN - SCOPUS:84918549545

VL - 32

SP - 257

EP - 308

JO - Handbook of Statistics

JF - Handbook of Statistics

SN - 0169-7161

ER -