A joint regression modeling framework for analyzing bivariate binary data in R. 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.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Azzalini, Adelchi; Kim, Hyoung-Moon; Kim, Hea-Jung: Sample selection models for discrete and other non-Gaussian response variables (2019)
- Filippou, Panagiota; Kneib, Thomas; Marra, Giampiero; Radice, Rosalba: A trivariate additive regression model with arbitrary link functions and varying correlation matrix (2019)
- Wojtyś, Małgorzata; Marra, Giampiero; Radice, Rosalba: Copula based generalized additive models for location, scale and shape with non-random sample selection (2018)
- Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
- Marra, Giampiero; Radice, Rosalba: A joint regression modeling framework for analyzing bivariate binary data in (\mathsfR) (2017)