Package: serp 0.2.4
serp: Smooth Effects on Response Penalty for CLM
A regularization method for the cumulative link models. The smooth-effect-on-response penalty (SERP) provides flexible modelling of the ordinal model by enabling the smooth transition from the general cumulative link model to a coarser form of the same model. In other words, as the tuning parameter goes from zero to infinity, the subject-specific effects associated with each variable in the model tend to a unique global effect. The parameter estimates of the general cumulative model are mostly unidentifiable or at least only identifiable within a range of the entire parameter space. Thus, by maximizing a penalized rather than the usual non-penalized log-likelihood, this and other numerical problems common with the general model are to a large extent eliminated. Fitting is via a modified Newton's method. Several standard model performance and descriptive methods are also available. For more details on the penalty implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.
Authors:
serp_0.2.4.tar.gz
serp_0.2.4.zip(r-4.5)serp_0.2.4.zip(r-4.4)serp_0.2.4.zip(r-4.3)
serp_0.2.4.tgz(r-4.4-any)serp_0.2.4.tgz(r-4.3-any)
serp_0.2.4.tar.gz(r-4.5-noble)serp_0.2.4.tar.gz(r-4.4-noble)
serp_0.2.4.tgz(r-4.4-emscripten)serp_0.2.4.tgz(r-4.3-emscripten)
serp.pdf |serp.html✨
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 3 years agofrom:5ac5400e21. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 14 2024 |
R-4.5-win | OK | Oct 14 2024 |
R-4.5-linux | OK | Oct 14 2024 |
R-4.4-win | OK | Oct 14 2024 |
R-4.4-mac | OK | Oct 14 2024 |
R-4.3-win | OK | Oct 14 2024 |
R-4.3-mac | OK | Oct 14 2024 |
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 |