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.
||R (≥ 3.1)
||Xiaoyan Wang, Gang Li
||Xiaoyan Wang <xywang at ucla.edu>
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