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) <arXiv:1611.03063> for more details.
Versions across snapshots
| Version | Repository | File | Size |
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0.1.0 |
rolling linux/jammy R-4.5 | PAmeasures_0.1.0.tar.gz |
28.2 KiB |
0.1.0 |
rolling linux/noble R-4.5 | PAmeasures_0.1.0.tar.gz |
28.2 KiB |
0.1.0 |
rolling source/ R- | PAmeasures_0.1.0.tar.gz |
5.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | PAmeasures_0.1.0.tar.gz |
28.2 KiB |
0.1.0 |
latest linux/noble R-4.5 | PAmeasures_0.1.0.tar.gz |
28.2 KiB |
0.1.0 |
latest source/ R- | PAmeasures_0.1.0.tar.gz |
5.6 KiB |
0.1.0 |
2026-04-26 source/ R- | PAmeasures_0.1.0.tar.gz |
5.6 KiB |
0.1.0 |
2026-04-23 source/ R- | PAmeasures_0.1.0.tar.gz |
5.6 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | PAmeasures_0.1.0.zip |
31.6 KiB |
0.1.0 |
2025-04-20 source/ R- | PAmeasures_0.1.0.tar.gz |
5.6 KiB |