Package: ordinalNet 2.12
Michael Wurm
ordinalNet: Penalized Ordinal Regression
Fits ordinal regression models with elastic net penalty. 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. For details, refer to Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06>.
Authors:
ordinalNet_2.12.tar.gz
ordinalNet_2.12.tar.gz(r-4.5-noble)ordinalNet_2.12.tar.gz(r-4.4-noble)
ordinalNet_2.12.tgz(r-4.4-emscripten)ordinalNet_2.12.tgz(r-4.3-emscripten)
ordinalNet.pdf |ordinalNet.html✨
ordinalNet/json (API)
# Install 'ordinalNet' in R: |
install.packages('ordinalNet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:331e53f31a. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-linux | OK | Nov 18 2024 |
Exports:ordinalNetordinalNetCVordinalNetTune
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
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Method to extract fitted coefficients from an "ordinalNet" object. | coef.ordinalNet |
Ordinal regression models with elastic net penalty | ordinalNet |
Uses K-fold cross validation to obtain out-of-sample log-likelihood and misclassification rates. Lambda is tuned within each cross validation fold. | ordinalNetCV |
Uses K-fold cross validation to obtain out-of-sample log-likelihood and misclassification rates for a sequence of lambda values. | ordinalNetTune |
Plot method for "ordinalNetTune" object. | plot.ordinalNetTune |
Predict method for an "ordinalNet" object | predict.ordinalNet |
Print method for an "ordinalNet" object. | print.ordinalNet |
Print method for an "ordinalNetCV" object. | print.ordinalNetCV |
Print method for an "ordinalNetTune" object. | print.ordinalNetTune |
Summary method for an "ordinalNet" object. | summary.ordinalNet |
Summary method for an "ordinalNetCV" object. | summary.ordinalNetCV |
Summary method for an "ordinalNetTune" object. | summary.ordinalNetTune |