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 (2017) <arXiv:1706.05003>.

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Description file content

Package
ordinalNet
Type
Package
Title
Penalized Ordinal Regression
Version
2.4
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) .
LazyData
TRUE
License
MIT + file LICENSE
Imports
stats, graphics
Suggests
testthat (>= 1.0.2), MASS (>= 7.3-45), glmnet (>= 2.0-5), penalized (>= 0.9-50), glmnetcr (>= 1.0.3), VGAM (>= 1.0-3), rms (>= 5.1-0)
RoxygenNote
6.0.1
NeedsCompilation
no
Packaged
2017-12-05 01:46:52 UTC; mike
Author
Michael Wurm [aut, cre], Paul Rathouz [aut], Bret Hanlon [aut]
Maintainer
Michael Wurm
Repository
CRAN
Date/Publication
2017-12-05 10:55:42 UTC

install.packages('ordinalNet')

2.4

8 days ago

Michael Wurm

MIT + file LICENSE

Imports

stats, graphics

Suggests

testthat (>= 1.0.2), MASS (>= 7.3-45), glmnet (>= 2.0-5), penalized (>= 0.9-50), glmnetcr (>= 1.0.3), VGAM (>= 1.0-3), rms (>= 5.1-0)

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