R package ordinalNet: Penalized Ordinal Regression. Fits ordinal regression models with elastic net penalty by coordinate descent. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm’s which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
- Derumigny, Alexis; Fermanian, Jean-David: A classification point-of-view about conditional Kendall’s tau (2019)
- M. Cristina Heredia-Gómez; Salvador García; Pedro Antonio Gutiérrez; Francisco Herrera: OCAPIS: R package for Ordinal Classification And Preprocessing In Scala (2018) arXiv
- Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2017) arXiv