2020
DOI: 10.18637/jss.v093.i04
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mvord: An R Package for Fitting Multivariate Ordinal Regression Models

Abstract: The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link. A flexible modeling framework for multiple ordinal measurements on the same subject is set up, which takes into consideration the dependence among the multiple observations by employing different error structures. Heterogeneity in the error structure across the subjects can be accounted for by the package, which allows for covariate depe… Show more

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Cited by 55 publications
(39 citation statements)
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“…This method has less restrictive assumptions than the ordinary least squares regression and is invariant to the choice of transformation of the outcome [ 48 ]. Using a model for multiple outcomes allows a direct comparison of the effect size estimates of BMI PRS on inattention and hyperactivity symptoms while taking their mutual correlation into account [ 49 ]. We report the effect estimates of BMI PRS on the multivariate outcomes adjusted for sex and the first ten genetic PCs.…”
Section: Methodsmentioning
confidence: 99%
“…This method has less restrictive assumptions than the ordinary least squares regression and is invariant to the choice of transformation of the outcome [ 48 ]. Using a model for multiple outcomes allows a direct comparison of the effect size estimates of BMI PRS on inattention and hyperactivity symptoms while taking their mutual correlation into account [ 49 ]. We report the effect estimates of BMI PRS on the multivariate outcomes adjusted for sex and the first ten genetic PCs.…”
Section: Methodsmentioning
confidence: 99%
“…We then fit a bivariate ordinal probit model to the ventral arc and subpubic concavity traits. We fit the model using the package “mvord” (Hirk, Hornik, Vana, & Genz, ) which uses composite likelihood estimation (Varin, Reid, & Firth, ). The estimated threshold parameters were 1.025, 1.807, 1.930, and 2.071 for VA and 1.606, 1.976, 2.000, and 2.185 for SPC.…”
mentioning
confidence: 99%
“…The multivariate models were generated in order to provide the most mathematically correct approach to combining indicators in practice (39) as the palate aging indicators are biologically correlated to one another. The multivariate models use composite likelihood estimation (40,41), which differentiates them from the univariate models in this paper that employ maximum likelihood estimation. Consistent with Konigsberg et al (36,42,43), a Lagrange multiplier test for more than two categories (44–46) was applied to test the assumption of ordinal probit models that the unobserved error term is log‐normally distributed.…”
Section: Methodsmentioning
confidence: 99%