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ordinalNet (version 2.7)

Penalized Ordinal Regression

Description

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 (2017) .

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Version

Install

install.packages('ordinalNet')

Monthly Downloads

813

Version

2.7

License

MIT + file LICENSE

Maintainer

Michael Wurm

Last Published

January 10th, 2020

Functions in ordinalNet (2.7)

summary.ordinalNetCV

Summary method for an "ordinalNetCV" object.
ordinalNet

Ordinal regression models with elastic net penalty
summary.ordinalNetTune

Summary method for an "ordinalNetTune" object.
predict.ordinalNet

Predict method for an "ordinalNet" object
plot.ordinalNetTune

Plot method for "ordinalNetTune" object.
print.ordinalNetTune

Print method for an "ordinalNetTune" object.
summary.ordinalNet

Summary method for an "ordinalNet" object.
print.ordinalNetCV

Print method for an "ordinalNetCV" object.
print.ordinalNet

Print method for an "ordinalNet" object.
ordinalNetCV

Uses K-fold cross validation to obtain out-of-sample log-likelihood and misclassification rates. Lambda is tuned within each cross validation fold.
ordinalNetTune

Uses K-fold cross validation to obtain out-of-sample log-likelihood and misclassification rates for a sequence of lambda values.
coef.ordinalNet

Method to extract fitted coefficients from an "ordinalNet" object.