FDR functions for permutation-based estimators, including pi0 as well as FDR confidence intervals. The confidence intervals account for dependencies between tests by the incorporation of an overdispersion parameter, which is estimated from the permuted data.
| Package: | fdrci |
| Type: | Package |
| Version: | 2.1 |
| Date: | 2018-02-21 |
| License: | Artistic-2.0 |
| LazyLoad: | yes |
This method is designed to compute FDR when a permutation-based approach has been utilized. The objective here is to identify a subset of positive tests that have corresponding statistics with a more exteme distribution than the permuted results, which are assumed to represent the null. The significance of the subset is described in terms of the FDR and uncertainty in the FDR estimate by computing a confidence interval. Say a set of p-values(or simply a set of test statistics) were recorded for a set of hypothesis tests, and data were permuted B times with test results generated for each permutation. The function fdr_od() can be used to estimate FDR and and a confidence interval along with pi0, the proportion of true null hypotheses, given a selected significance threshold. The function fdrTbl()uses fdr_od() to create a table of results over a sequence of possible significance thresholds. Finally, the function FDRplot will plot results from fdrTbl(), facilitating the selection of a final significance threshold.
Millstein J, Volfson D. 2013. Computationally efficient permutation-based confidence interval estimation for tail-area FDR. Frontiers in Genetics | Statistical Genetics and Methodology 4(179):1-11.