ordinal: Regression Models for Ordinal Data. Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.
References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Ekstrøm, Claus Thorn: The R primer (2016)
- Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
- Christensen, Rune Haubo Bojesen; Brockhoff, Per Bruun: Analysis of sensory ratings data with cumulative link models (2013)