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We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function's ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function's bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <doi:10.48550/arXiv.1611.03063> for more details.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.1) |
| Imports: | survival, stats |
| Suggests: | testthat |
| Published: | 2018-01-22 |
| DOI: | 10.32614/CRAN.package.PAmeasures |
| Author: | Xiaoyan Wang, Gang Li |
| Maintainer: | Xiaoyan Wang <xywang at ucla.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | PAmeasures results |
| Reference manual: | PAmeasures.pdf |
| Package source: | PAmeasures_0.1.0.tar.gz |
| Windows binaries: | r-devel: PAmeasures_0.1.0.zip, r-release: PAmeasures_0.1.0.zip, r-oldrel: PAmeasures_0.1.0.zip |
| macOS binaries: | r-release (arm64): PAmeasures_0.1.0.tgz, r-oldrel (arm64): PAmeasures_0.1.0.tgz, r-release (x86_64): PAmeasures_0.1.0.tgz, r-oldrel (x86_64): PAmeasures_0.1.0.tgz |
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