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) for more details.


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0.1.0 by Xiaoyan Wang, 3 years ago

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Authors: Xiaoyan Wang , Gang Li

Documentation:   PDF Manual  

GPL-3 license

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