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PAmeasures
View on CRAN: Click
here
Download and install PAmeasures package within the R console
Install from CRAN:
install.packages("PAmeasures")
Install from Github:
library("remotes")
install_github("cran/PAmeasures") Install by package version:
library("remotes")
install_version("PAmeasures", "0.1.0") Attach the package and use:
library("PAmeasures")
Maintained by
Xiaoyan Wang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-01-22
Latest Update: 2018-01-22
Description:
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.
How to cite:
Xiaoyan Wang (2018). PAmeasures: Prediction and Accuracy Measures for Nonlinear Models and for Right-Censored Time-to-Event Data. R package version 0.1.0, https://cran.r-project.org/web/packages/PAmeasures. Accessed 05 Mar. 2026.
Previous versions and publish date:
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Complete documentation for PAmeasures
Functions, R codes and Examples using
the PAmeasures R package
Some associated functions: moore . pam.censor . pam.coxph . pam.nlm . pam.survreg .
Some associated R codes: moore.R . pam.censor.R . pam.coxph.R . pam.nlm.R . pam.survreg.R . Full PAmeasures package functions and examples
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