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A joint regression modeling framework for analyzing bivariate binary data in R

Marra, G. & Radice, R. ORCID: 0000-0002-6316-3961 (2017). A joint regression modeling framework for analyzing bivariate binary data in R. Dependence Modeling, 5(1), pp. 268-294. doi: 10.1515/demo-2017-0016


We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

Publication Type: Article
Additional Information: © 2017. Giampiero Marra and Rosalba Radice, published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.
Publisher Keywords: binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School > Actuarial Science & Insurance
Text - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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