| DiscreteFDR {DiscreteFDR} | R Documentation |
FDR-based Multiple Testing Procedures with Adaptation for Discrete Tests
Description
This package implements the [HSU], [HSD],
[AHSU], [AHSD] and [HBR-\lambda] procedures for
discrete tests (see References).
Details
The functions are reorganized from the reference paper in the following way.
discrete.BH() (for Discrete Benjamini-Hochberg) implements
[HSU], [HSD], [AHSU] and [AHSD], while DBR() (for Discrete
Blanchard-Roquain) implements [HBR-\lambda]. DBH() and ADBH()
are wrapper functions for discrete.BH() to access [HSU] and [HSD], as
well as [AHSU] and [AHSD] directly.
This package is part of a package family to which the
DiscreteDatasets and
DiscreteTests packages also
belong. The latter allows to compute p-values and their respective supports
for various tests. The objects that contain these results can be used
directly by the discrete.BH(), DBH(), ADBH() and DBR()
functions. Alternatively, these functions also accept a vector of raw
observed p-values and a list of the respective discrete supports of the CDFs
of the p-values.
Note: The former function fisher.pvalues.support(), which allows to
compute such p-values and supports in the framework of a Fisher's exact test,
is now deprecated and should not be used anymore. It has been replaced by
generate.pvalues().
The same applies for the function fast.Discrete(), which is a wrapper for
fisher.pvalues.support() and discrete.BH() and allows to apply
discrete procedures directly to a data set of contingency tables and perform
data pre-processing before p-values are computed. It is also now deprecated
and has been replaced by direct.discrete.BH(), but for more flexibility,
users may employ pipes, e.g.
data |>
DiscreteDatasets::reconstruct_*(<args>) |>
DiscreteTests::*.test.pv(<args>) |>
discrete.BH(<args>).
Author(s)
Maintainer: Florian Junge diso.fbmn@h-da.de [contributor]
Authors:
Sebastian Döhler [contributor]
Guillermo Durand [contributor]
Other contributors:
Etienne Roquain [contributor]
Christina Kihn [contributor]
References
Döhler, S., Durand, G., & Roquain, E. (2018). New FDR bounds for discrete and heterogeneous tests. Electronic Journal of Statistics, 12(1), pp. 1867-1900. doi:10.1214/18-EJS1441
G. Blanchard and E. Roquain (2009). Adaptive false discovery rate control under independence and dependence. Journal of Machine Learning Research, 10, pp. 2837-2871.
See Also
Useful links: