glmnetcr-package {glmnetcr}

Penalized Constrained Continuation Ratio Models for Ordinal Response Prediction using 'glmnet'

Description

This package provides a function glmnetcr for fitting penalized constrained continuation ratio models for predicting an ordinal response and associated methods for plotting, printing, extracting predicted classes and probabilities, and extracting estimated coefficients for selected models in the regularization path.

Details

The DESCRIPTION file:

Package:	glmnetcr
Type:	Package
Title:	Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response
Version:	1.0.6
Date:	2020-07-02
Author:	Kellie J. Archer
Maintainer:	Kellie J. Archer <[e-mail]>
Description:	Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
License:	GPL-2
Depends:	R (>= 2.10), glmnet
Suggests:	tools
BuildResaveData:	best
LazyLoad:	yes

Index of help topics:

coef.glmnetcr           Extract All Model Coefficients
diabetes                Gene Expression in Normal, Impaired Fasting
                        Glucose, and Type II Diabetic Males
fitted.glmnetcr         AIC, BIC, Predicted Class, and Fitted
                        Probabilities of Class Membership
glmnetcr                Fit a Penalized Constrained Continuation Ratio
                        Model Using Lasso or Elasticnet Regularization
                        Via 'glmnet'
glmnetcr-package        Penalized Constrained Continuation Ratio Models
                        for Ordinal Response Prediction using 'glmnet'
nonzero.glmnetcr        Extract Non-Zero Model Coefficients
plot.glmnetcr           Plots the Regularization Path Computed
predict.glmnetcr        AIC, BIC, Predicted Class, and Fitted
                        Probabilities for All Models
print.glmnetcr          Print a 'glmnetcr' Object
select.glmnetcr         Select Step of Optimal Fitted AIC or BIC CR
                        Model

This package contains functions for fitting penalized constrained continuation ratio models and extracting estimated coefficients, predicted class, and fitted probabilities. The model and methods can be used when the response to be predicted is ordinal, and is particularly relevant when there are more covariates than observations.

Author(s)

Kellie J. Archer Kellie J. Archer <[e-mail]>

Maintainer: Kellie J. Archer <[e-mail]> Kellie J. Archer <[e-mail]>

References

Archer K.J., Williams A.A.A. (2012) L1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets. Statistics in Medicine, 31(14), 1464-74.

See Also

See also glmnet ~~

Examples

data(diabetes)
x <- diabetes[, 2:dim(diabetes)[2]]
y <- diabetes$y
glmnet.fit <- glmnetcr(x, y)
AIC <- select.glmnetcr(glmnet.fit, which="AIC")
fitted(glmnet.fit, s=AIC)