fdrci-package: Permutation-Based FDR Point and Confidence Interval...
In USCbiostats/fdrci: Permutation-Based FDR Point and Confidence Interval Estimation
fdrci-package R Documentation
Permutation-Based FDR Point and Confidence Interval Estimation
Description
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. The package also includes a parametric
analog of the same approach.
Details
Package: fdrci
Type: Package
Version:
2.4
Date: 2022-10-17
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.
Author(s)
Joshua Millstein
Maintainer: Joshua Millstein <joshua.millstein@usc.edu> Joshua Millstein
References
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.
Millstein, J., Battaglin, F., Arai, H., Zhang, W., Jayachandran, P.,
Soni, S., Parikh, A.R., Mancao, C. and Lenz, H.J., 2022. fdrci: FDR
confidence interval selection and adjustment for large-scale hypothesis
testing. Bioinformatics advances, 2(1), p.vbac047.
USCbiostats/fdrci documentation built on Oct. 22, 2022, 11:44 p.m.
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| fdrci-package | R Documentation |
Permutation-Based FDR Point and Confidence Interval Estimation
Description
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. The package also includes a parametric analog of the same approach.
Details
| Package: | fdrci |
| Type: | Package |
| Version: | 2.4 |
| Date: | 2022-10-17 |
| 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.
Author(s)
Joshua Millstein
Maintainer: Joshua Millstein <joshua.millstein@usc.edu> Joshua Millstein
References
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.
Millstein, J., Battaglin, F., Arai, H., Zhang, W., Jayachandran, P., Soni, S., Parikh, A.R., Mancao, C. and Lenz, H.J., 2022. fdrci: FDR confidence interval selection and adjustment for large-scale hypothesis testing. Bioinformatics advances, 2(1), p.vbac047.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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