R-universe search: topic:ordinal-regression

Showing 5 of total 5 results (show query)

koalaverse

sure:Surrogate Residuals for Ordinal and General Regression Models

An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017, <doi:https://doi.org/10.1080/01621459.2017.1292915>) and Greenwell et al. (2017, <https://journal.r-project.org/archive/2018/RJ-2018-004/index.html>). These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available.

Maintained by Brandon Greenwell. Last updated 30 days ago.

categorical-data diagnostics ordinal-regression residuals

9 stars 5.58 score 47 scripts 1 dependents

benkeser

drord:Doubly-Robust Estimators for Ordinal Outcomes

Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes (Benkeser, Diaz, Luedtke 2020 <doi:10.1111/biom.13377>)

Maintained by David Benkeser. Last updated 4 years ago.

causal-inference covid-19 double-robust mann-whitney ordinal-regression

4 stars 4.38 score 12 scripts

xsswang

remiod:Reference-Based Multiple Imputation for Ordinal/Binary Response

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.

Maintained by Tony Wang. Last updated 2 years ago.

bayesian control-based copy-reference delta-adjustment generalized-linear-models glm jags jump-to-reference mcmc missing-at-random missing-data missing-not-at-random multiple-imputation non-ignorable ordinal-regression pattern-mixture-model reference-based statistics cpp

4.30 score 3 scripts

ejikeugba

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>.

Maintained by Ejike R. Ugba. Last updated 4 months ago.

categorical-data ordinal-regression penalized-regression proportional-odds-regression regularization-techniques

1 stars 3.94 score 44 scripts

ejikeugba

gofcat:Goodness-of-Fit Measures for Categorical Response Models

A post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

Maintained by Ejike R. Ugba. Last updated 2 years ago.

brant-test brier-scores hosmer-lemeshow-test likelihood-ratio-test lipsitz-test log-loss-score-metric logistic-regression misclassification ordinal-regression proportional-odds-test pseudo-r2 pulkstenis-robinson-test

2 stars 3.18 score 15 scripts