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

An R package for constructing SUrrogate-based REsiduals and diagnostics for ordinal and general regression models; based on the approach described in Dungang and Zhang (2017).

Installation

The sure package is currently listed on CRAN and can easily be installed:

# Install from CRAN (recommended)
install.packages("sure")
  
# Alternatively, install the development version from GitHub
if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("AFIT-R/sure")

References

Liu, Dungang and Zhang, Heping. Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach. Journal of the American Statistical Association (accepted). URL http://www.tandfonline.com/doi/abs/10.1080/01621459.2017.1292915?journalCode=uasa20