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SurrogateRsq  

Goodness-of-Fit Analysis for Categorical Data using the Surrogate R-Squared
View on CRAN: Click here


Download and install SurrogateRsq package within the R console
Install from CRAN:
install.packages("SurrogateRsq")

Install from Github:
library("remotes")
install_github("cran/SurrogateRsq")

Install by package version:
library("remotes")
install_version("SurrogateRsq", "0.2.1")



Attach the package and use:
library("SurrogateRsq")
Maintained by
Xiaorui (Jeremy) Zhu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-15
Latest Update: 2023-04-24
Description:
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.
How to cite:
Xiaorui (Jeremy) Zhu (2023). SurrogateRsq: Goodness-of-Fit Analysis for Categorical Data using the Surrogate R-Squared. R package version 0.2.1, https://cran.r-project.org/web/packages/SurrogateRsq. Accessed 02 Feb. 2025.
Previous versions and publish date:
0.2.0 (2023-02-15 13:20)
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