Goodness-of-Fit Measures for Categorical Response Models.

gofcat

Overview

Crucial post-estimation (goodness-of-fit) tests for some widely used categorical response models (CRM) are implemented in this package. It currently supports inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm(). 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.

Example

require(serp)
set.seed(1)
n <- 200
dt <- data.frame(y = ordered(rbinom(n,2,0.5)), x1 = factor(rbinom(n,2,0.7)), x2 = runif(n))
sp <- serp(y ~ x1 + x2, slope="parallel", link = "logit", reverse= TRUE, data = dt)

## Goodness-of-fit
# Hosmer-Lemeshow test
hosmerlem(sp, tables = TRUE)
hosmerlem(sp, tables = TRUE, customFreq = rep(20,10))

# Lipsitz test
lipsitz(sp)
lipsitz(sp, customFreq = rep(20, 10))

# Pulkstenis-Robinson test
pulkroben(sp, test = "chisq", tables = TRUE)
pulkroben(sp, test = "deviance", tables = TRUE)

## Proportional odds test
brant.test(sp)
brant.test(sp, global = TRUE, call = TRUE)
LR.test(sp, call = TRUE)

## Error metrics
erroR(sp, type = "brier")
erroR(sp, type = "logloss")
erroR(sp, type = "misclass")

# with dataframe and custom threshold
df <- data.frame(y, sp$fitted.values)
erroR(df, type = "misclass", thresh = 0.7)

## Summary metrics
Rsquared(sp, measure = "ugba")
Rsquared(sp, measure = "mcfadden")

Installation and Use

Before installing gofcat, it is encouraged to have a recent version of R installed. The released version of gofcat can be installed from CRAN with:

install.packages("gofcat")

or the development version from GitHub with:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("ejikeugba/gofcat")

Load gofcat into R environment with:

library(gofcat)

Community Guidelines

Pull requests are welcomed! Please submit your contributions to gofcat through the list of Pull Requests, following the contributing guidelines. To report issues and/or seek support, please file a new ticket in the issue tracker, and expect a feedback ASAP!

Code of Conduct

Please note that gofcat is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Fagerland, M. W. and Hosmer, D. W. (2017). How to test for goodness of fit in ordinal logistic regression models. Stata Journal, 17, 668-686.

Ugba, E. R. and Gertheiss, J. (2018). An Augmented Likelihood Ratio Index for Categorical Response Models. In Proceedings of 33rd International Workshop on Statistical Modelling, Bristol, 293-298. http://www.statmod.org/workshops_archive_proceedings_2018.html

Ugba, E. R. (2021). serp: An R package for smoothing in ordinal regression Journal of Open Source Software, 6(66), 3705. https://doi.org/10.21105/joss.03705