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

Marra Giampiero () and Radice Rosalba ()
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Marra Giampiero: Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
Radice Rosalba: Department of Economics, Mathematics and Statistics, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK

Dependence Modeling, 2017, vol. 5, issue 1, 268-294

Abstract: 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.

Keywords: binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:5:y:2017:i:1:p:268-294:n:16

DOI: 10.1515/demo-2017-0016

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