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 (2018) and Greenwell et al. (2018) . 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 'plot' methods for producing standard diagnostic plots using lightweight base R graphics (via 'tinyplot'). The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM', binary and other generalized linear models fit with 'glm', and multinomial models or general classifiers via a matrix of predicted class probabilities.

sure

Surrogate residuals and diagnostics for ordinal and general regression models, based on the approach of Liu and Zhang (2018).

Surrogate residuals for cumulative link models from MASS, ordinal, rms, and VGAM, and for glm() fits.
Multinomial models and general classifiers via a matrix of predicted class probabilities.
Latent-variable and jittering methods, with bootstrap replicates.
Q-Q, residual-vs-fitted, and residual-vs-covariate plots built on tinyplot; no non-base hard dependencies.

Installation

# From CRAN (recommended)
install.packages("sure")

# Latest development version, from r-universe
install.packages("sure", repos = c("https://bgreenwell.r-universe.dev", "https://cloud.r-project.org"))

Quick start

library(sure)

# Simulate an ordinal response with a quadratic true relationship
set.seed(101)
df <- sim_data(n = 500, type = "quadratic")

# Fit a (misspecified) proportional odds model
fit <- MASS::polr(y ~ x, data = df)

# Compute surrogate-based residuals and plot the diagnostics
res <- resids(fit)
plot(res, what = "covariate", covariate = df$x, xlab = "x")

Documentation

Package website: https://bgreenwell.github.io/sure/
Introductory vignette: vignette("sure", package = "sure")
Greenwell, B. M., McCarthy, A. J., Boehmke, B. C., and Liu, D. (2018). Residuals and diagnostics for binary and ordinal regression models: An introduction to the sure package. The R Journal, 10(1). https://doi.org/10.32614/RJ-2018-004
Liu, D. and Zhang, H. (2018). Residuals and diagnostics for ordinal regression models: A surrogate approach. Journal of the American Statistical Association, 113(522), 845-854. https://doi.org/10.1080/01621459.2017.1292915

Development

devel is the default branch and holds ongoing development; main holds stable releases only. Please open pull requests against devel and report bugs at https://github.com/bgreenwell/sure/issues.