PredictABEL: Assessment of Risk Prediction Models

We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models.

Package details

AuthorSuman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens
MaintainerSuman Kundu <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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PredictABEL documentation built on March 13, 2020, 3:15 a.m.