DiscreteFDR {DiscreteFDR}R Documentation

DiscreteFDR

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. Their main arguments are a vector of raw observed p-values, and a list of the same length, whose elements are the discrete supports of the CDFs of the p-values.

The function fisher.pvalues.support allows to compute such p-values and support in the framework of a Fisher's exact test of association. It has been inspired by a help page of the package discreteMTP, which is no longer available on CRAN.

The function fast.Discrete is a wrapper for fisher.pvalues.support and discrete.BH which allows to apply discrete procedures directly to a data set of contingency tables.

We also provide the amnesia data set, used in our examples and in our paper. It is basically the amnesia data set of package discreteMTP (no longer on CRAN), but slightly reformatted such that each line represents a 2x2 contingency table.

No other function of the package should be used directly, as they are only internal functions called by the main ones.

Author(s)

Maintainer: Darmstadt Institute of Statistics and Operations Research diso.fbmn@h-da.de

Authors:

Other contributors:

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:

[Package DiscreteFDR version 1.3.7 Index]