Marra, G;
Radice, R;
(2017)
Joint regression modeling framework for analyzing bivariate binary data in R.
Dependence Modeling
, 5
(1)
pp. 268-294.
10.1515/demo-2017-0016.
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[Dependence Modeling] A joint regression modeling framework for analyzing bivariate binary data in R.pdf - Published Version Download (1MB) | Preview |
Abstract
We discuss some of the features of the R add-on package GJRM which implements a ffiexible 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 ffiexible 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.
| Type: | Article |
|---|---|
| Title: | Joint regression modeling framework for analyzing bivariate binary data in R |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1515/demo-2017-0016 |
| Publisher version: | https://doi.org/10.1515/demo-2017-0016 |
| Language: | English |
| Additional information: | This is an open access article. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. |
| Keywords: | Binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10074530 |
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