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PAmeasures  

Prediction and Accuracy Measures for Nonlinear Models and for Right-Censored Time-to-Event Data
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]
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 07 Nov. 2024.
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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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