PAmeasures: Prediction and Accuracy Measures for Nonlinear Models and for Right-Censored Time-to-Event Data

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>
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:, r-release:, r-oldrel:
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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