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crov (version 0.3.0)

Constrained Regression Model for an Ordinal Response and Ordinal Predictors

Description

Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) . The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then one of the available methods will either establish it or drop the monotonicity assumption. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.

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Version

Install

install.packages('crov')

Monthly Downloads

167

Version

0.3.0

License

GPL-2

Maintainer

Javier Espinosa

Last Published

August 25th, 2023

Functions in crov (0.3.0)

crovData

Real data example
confRegUCRandUCCR

Parameter Vector in Confidence Regions UCR and/or UCCR
monoTestBonf

Monotonicity test
monoTestConfReg

Monotonicity test using confidence regions
mdcp

Monotonicity Direction Classification (MDC) procedure
plotCMLE

Plot unconstrained and constrained proportional odds logit model
confRegCCR

Parameter Vector in Confidence Region CCR