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

Giampiero Marra; Rosalba Radice

Dependence Modeling (2017)

  • Volume: 5, Issue: 1, page 268-294
  • ISSN: 2300-2298

Abstract

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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.

How to cite

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Giampiero Marra, and Rosalba Radice. "A joint regression modeling framework for analyzing bivariate binary data in R." Dependence Modeling 5.1 (2017): 268-294. <http://eudml.org/doc/288494>.

@article{GiampieroMarra2017,
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.},
author = {Giampiero Marra, Rosalba Radice},
journal = {Dependence Modeling},
keywords = {binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation},
language = {eng},
number = {1},
pages = {268-294},
title = {A joint regression modeling framework for analyzing bivariate binary data in R},
url = {http://eudml.org/doc/288494},
volume = {5},
year = {2017},
}

TY - JOUR
AU - Giampiero Marra
AU - Rosalba Radice
TI - A joint regression modeling framework for analyzing bivariate binary data in R
JO - Dependence Modeling
PY - 2017
VL - 5
IS - 1
SP - 268
EP - 294
AB - 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.
LA - eng
KW - binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation
UR - http://eudml.org/doc/288494
ER -

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