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.
| Version: |
1.2-4 |
| Depends: |
R (≥ 2.12.0) |
| Imports: |
Hmisc, ROCR, PBSmodelling, lazyeval, methods |
| Published: |
2020-03-09 |
| Author: |
Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens |
| Maintainer: |
Suman Kundu <suman_math at yahoo.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
PredictABEL results |
Documentation:
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