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

• Autorzy: Giampiero Marra , Rosalba Radice
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation

Kategorie tematyczne

• 62H99: None of the above, but in this section
• 62J02: General nonlinear regression

• Wydawca: De Gruyter Open
• Czasopismo: Dependence Modeling
• Rocznik: 2017
• Tom: 5
• Numer: 1
• Strony: 268-294

Daty

wydano

2017-12-20

otrzymano

2017-09-22

zaakceptowano

2017-10-18

online

2017-12-07

Twórcy

autor

Giampiero Marra

• WC1E 6BT,

autor

Rosalba Radice

• WC1E 7HX,

Identyfikatory

DOI

10.1515/demo-2017-0016