glmpathcr-package: Fit a Penalized Continuation Ratio Model for Predicting an...

In glmpathcr: Fit a Penalized Continuation Ratio Model for Predicting an Ordinal Response

Fit a Penalized Continuation Ratio Model for Predicting an Ordinal Response: Penalized L1 continuation Ratio Models for Ordinal Response Prediction in High-dimensional Data Settings

Description

This package provides a function glmpathcr for fitting a penalized L1 continuation ratio model for predicting an ordinal response and associated methods for plotting, getting predicted values, estimating coefficients for selected models.

Details

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This package contains functions for fitting a penalized continuation ratio model 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 glmpath

Examples

   data(diabetes)
   x <- diabetes[, 2:dim(diabetes)[2]]
   y <- diabetes$y
   fit <- glmpathcr(x, y)

glmpathcr documentation built on July 9, 2023, 6:57 p.m.