gofcat

Overview

Post-estimation (goodness-of-fit) tests for some widely used categorical response models (CRM). Package currently supports inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm(). Available tests include the Hosmer-Lemeshow, the Lipsitz and the Pulkstenis-Robinson tests for the binary, multinomial and ordinal logistic regression models. The proportional odds, adjacent category, and constrained continuation-ratio models are particularly supported at the 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 error measurement metrics (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

Ugba, E. R. and Gertheiss, J. (2023). A Modification of McFadden's $R^2$ for Binary and Ordinal Response Models. Commun. Stat. Appl. Methods, 30, 49-63. https://doi.org/10.29220/CSAM.2023.30.1.049

Ugba, E. R. (2022). gofcat: An R package for goodness-of-fit of categorical response models. Journal of Open Source Software, 7(76), 4382, https://doi.org/10.21105/joss.04382

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.