Package: serp 0.2.5
serp: Smooth Effects on Response Penalty for CLM
Implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.
Authors:
serp_0.2.5.tar.gz
serp_0.2.5.zip(r-4.7)serp_0.2.5.zip(r-4.6)serp_0.2.5.zip(r-4.5)
serp_0.2.5.tgz(r-4.6-any)serp_0.2.5.tgz(r-4.5-any)
serp_0.2.5.tar.gz(r-4.7-any)serp_0.2.5.tar.gz(r-4.6-any)
serp_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
serp/json (API)
NEWS
| # Install 'serp' in R: |
| install.packages('serp', repos = c('https://ejikeugba.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ejikeugba/serp/issues
- wine - Bitterness of wine dataset
categorical-dataordinal-regressionpenalized-regressionproportional-odds-regressionregularization-techniques
Last updated from:b6eec7672a. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 152 | ||
| source / vignettes | OK | 172 | ||
| linux-release-x86_64 | OK | 145 | ||
| macos-release-arm64 | OK | 100 | ||
| macos-oldrel-arm64 | OK | 142 | ||
| windows-devel | OK | 108 | ||
| windows-release | OK | 96 | ||
| windows-oldrel | OK | 122 | ||
| wasm-release | OK | 125 |
Exports:serpserp.control
Dependencies:crayonlatticeMASSMatrixnlmenumDerivordinalucminf
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| AIC for a fitted serp object | AIC.serp |
| ANOVA method for a fitted serp object | anova.serp |
| BIC for a fitted serp object | BIC.serp |
| Coefficients for a fitted serp object | coef.serp coefficients.serp |
| Confidence interval for a fitted serp object | confint.serp |
| Log-likelihood for a fitted serp object | logLik.serp |
| Prediction from fitted serp model | predict.serp |
| Print method for a fitted serp object | print.serp |
| Print method for an object of class summary.serp | print.summary.serp |
| Smooth Effects on Response Penalty for CLM | serp |
| Control parameters for a fitted serp object | serp.control |
| Summary method for a fitted serp object. | summary.serp |
| Variance covariance matrix for a fitted serp object | vcov.serp |
| Bitterness of wine dataset | wine |
