Introduction | Penalized models | Implementation | Example | Summary | Acknowledgments
Package: glmnetcr 1.0.7
glmnetcr: Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response
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.
Authors:
glmnetcr_1.0.7.tar.gz
glmnetcr_1.0.7.zip(r-4.7)glmnetcr_1.0.7.zip(r-4.6)glmnetcr_1.0.7.zip(r-4.5)
glmnetcr_1.0.7.tgz(r-4.6-any)glmnetcr_1.0.7.tgz(r-4.5-any)
glmnetcr_1.0.7.tar.gz(r-4.7-any)glmnetcr_1.0.7.tar.gz(r-4.6-any)
glmnetcr_1.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
glmnetcr/json (API)
| # Install 'glmnetcr' in R: |
| install.packages('glmnetcr', repos = c('https://kelliejarcher.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kelliejarcher/glmnetcr/issues
Datasets:
- diabetes - Gene Expression in Normal, Impaired Fasting Glucose, and Type II Diabetic Males
Last updated from:401f8f587c. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 148 | ||
| source / vignettes | OK | 164 | ||
| linux-release-x86_64 | OK | 159 | ||
| macos-release-arm64 | OK | 244 | ||
| macos-oldrel-arm64 | OK | 176 | ||
| windows-devel | OK | 116 | ||
| windows-release | OK | 119 | ||
| windows-oldrel | OK | 120 | ||
| wasm-release | OK | 135 |
Exports:coef.glmnetcrcr.backwardcr.forwardfitted.glmnetcrglmnetcrnonzero.glmnetcrplot.glmnetcrpredict.glmnetcrprint.glmnetcrselect.glmnetcr
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Penalized Constrained Continuation Ratio Models for Ordinal Response Prediction using 'glmnet' | glmnetcr-package |
| Extract All Model Coefficients | coef.glmnetcr |
| Gene Expression in Normal, Impaired Fasting Glucose, and Type II Diabetic Males | diabetes |
| AIC, BIC, Predicted Class, and Fitted Probabilities of Class Membership | fitted.glmnetcr |
| Fit a Penalized Constrained Continuation Ratio Model Using Lasso or Elasticnet Regularization Via 'glmnet' | glmnetcr |
| Extract Non-Zero Model Coefficients | nonzero.glmnetcr |
| Plots the Regularization Path Computed | plot.glmnetcr |
| AIC, BIC, Predicted Class, and Fitted Probabilities for All Models | predict.glmnetcr |
| Print a 'glmnetcr' Object | print.glmnetcr |
| Select Step of Optimal Fitted AIC or BIC CR Model | select.glmnetcr |
