Package: PAmeasures 0.1.0
Xiaoyan Wang
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.
Authors:
PAmeasures_0.1.0.tar.gz
PAmeasures_0.1.0.tar.gz(r-4.5-noble)PAmeasures_0.1.0.tar.gz(r-4.4-noble)
PAmeasures_0.1.0.tgz(r-4.4-emscripten)PAmeasures_0.1.0.tgz(r-4.3-emscripten)
PAmeasures.pdf |PAmeasures.html✨
PAmeasures/json (API)
# Install 'PAmeasures' in R: |
install.packages('PAmeasures', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- moore - Moore's Law data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:d73a4eec65. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 10 2024 |
R-4.5-linux | OK | Oct 10 2024 |