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.
| Version: |
0.2.1 |
| Depends: |
R (≥ 3.5.0), MASS (≥ 7.3-54), PAsso (≥ 0.1.10), progress (≥ 1.2.0), scales (≥ 1.1.1) |
| Suggests: |
R.rsp, knitr, rmarkdown, testthat (≥ 3.0.0), dplyr (≥
1.1.1) |
| Published: |
2023-04-24 |
| DOI: |
10.32614/CRAN.package.SurrogateRsq |
| Author: |
Xiaorui (Jeremy) Zhu [aut, cre, cph],
Dungang Liu [ctb],
Zewei Lin [ctb],
Brandon Greenwell [ctb] |
| Maintainer: |
Xiaorui (Jeremy) Zhu <zhuxiaorui1989 at gmail.com> |
| BugReports: |
https://github.com/XiaoruiZhu/SurrogateRsq/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://xiaorui.site/SurrogateRsq/,
http://xiaorui.site/SurrogateRsq/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
SurrogateRsq results |
Documentation:
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