Goodness-of-Fit Measures for Categorical Response Models
Print method for an object of class hosmerlem
Print method for an object of class lipsitz
Print method for an object of class LRT
Print method for an object of class pulkroben
Print method for an object of class r-squared
Pulkstenis-Robinson Test for Categorical Response Models
R-squared for Categorical Response Models
Brant Test of the Proportional Odds Assumption
Performance metrics for categorical models
Hosmer-Lemeshow Test for Categorical Response Models
Lipsitz Test for Categorical Response Models
Likelihood Ratio Test of the Proportional Odds Assumption
Print method for an object of class brant
Print method for an object of class erroR
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>.