PredictABEL (1.1)

Assessment of risk prediction models.

PredictABEL includes 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.

Maintainer: Suman Kundu
Author(s): Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens

License: GPL (>= 2)

Uses: epitools, Hmisc, PBSmodelling, ROCR, GenABEL
Reverse suggests: GenABEL

Released almost 9 years ago.