R package 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.
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References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Agresti, Alan; Kateri, Maria: Ordinal probability effect measures for group comparisons in multinomial cumulative link models (2017)
- Ekstrøm, Claus Thorn: The R primer (2016)
- Irvine, Kathryn M.; Rodhouse, T.J.; Keren, Ilai N.: Extending ordinal regression with a latent zero-augmented beta distribution (2016)
- Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
- Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
- Hou, Jiayi; Archer, Kellie J.: Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data (2015)
- Wright, Marvin N.; Ziegler, Andreas: Multiple censored data in dentistry: a new statistical model for analyzing lesion size in randomized controlled trials (2015)
- Christensen, Rune Haubo Bojesen; Brockhoff, Per Bruun: Analysis of sensory ratings data with cumulative link models (2013)