SurrogateRsq: Goodness-of-Fit Analysis for Categorical Data using the Surrogate R-Squared

To assess and compare the models' goodness of fit, R-squared is one of the most popular measures. For categorical data analysis, however, no universally adopted R-squared measure can resemble the ordinary least square (OLS) R-squared for linear models with continuous data. This package implement the surrogate R-squared measure for categorical data analysis, which is proposed in the study of Dungang Liu, Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022) <doi:10.1111/bmsp.12289>. It can generate a point or interval measure of the surrogate R-squared. It can also provide a ranking measure of the percentage contribution of each variable to the overall surrogate R-squared. This ranking assessment allows one to check the importance of each variable in terms of their explained variance. This package can be jointly used with other existing R packages for variable selection and model diagnostics in the model-building process.

Package details

AuthorXiaorui (Jeremy) Zhu [aut, cre, cph], Dungang Liu [ctb], Zewei Lin [ctb], Brandon Greenwell [ctb]
MaintainerXiaorui (Jeremy) Zhu <zhuxiaorui1989@gmail.com>
LicenseGPL (>= 2)
Version0.2.1
URL https://xiaorui.site/SurrogateRsq/ http://xiaorui.site/SurrogateRsq/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SurrogateRsq")

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SurrogateRsq documentation built on April 24, 2023, 9:10 a.m.