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
Author:	Xiaoyan Wang, Gang Li
Maintainer:	Xiaoyan Wang <xywang at ucla.edu>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	PAmeasures results

Documentation:

Reference manual:

PAmeasures.pdf

Downloads:

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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