fdrDiscreteNull: False Discovery Rate Procedure Under Discrete Null Distributions
Version 1.0

It is known that current false discovery rate (FDR) procedures can be very conservative when applied to p-values (and test statistics) with discrete (and heterogeneous) null distributions. This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing. The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed. The package implements the method for three types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, (c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means. It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.

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AuthorXiongzhi Chen and R.W. Doerge <doerge@purdue.edu>
Date of publication2015-02-16 14:10:01
MaintainerXiongzhi Chen <xiongzhi@princeton.edu>
LicenseLGPL
Version1.0
URL http://www.princeton.edu/~xiongzhi/fdrDiscreteNull
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("fdrDiscreteNull")

Man pages

arabidopsisE: Gene expression data for Arabidopsis thaliana
arabidopsisM: Methylation data for Arabidopsis thaliana
GeneralizedEstimatorsGrouped: Weighted multiple hypothesis testing by generalized...
GeneralizedFDREstimators: Multiple hypothesis testing by generalized estimators.

Functions

BHFDRApp Source code
CheckIt Source code
CountsNormalizerInEdgeR Source code
DGEList_adjusted Source code
Div_Appr_Unif Source code
Div_Ref_Unif Source code
GeneralizedEstimatorsGrouped Man page Source code
GeneralizedFDREstimators Man page Source code
GetDivergenceMatrix Source code
GetPseudoCountsLibsizeMeansSizesFromNBPpack Source code
StAndChenFDREstimatorApp Source code
arabidopsisE Man page
arabidopsisM Man page
deviations Source code
deviationsExtPvalSupp Source code
eNetBuilder Source code
eNetFull Source code
equalizeLibSizes_adjusted Source code
estimateCommonDisp_adjusted Source code
fulltable Source code
getcellcountsandmarginals Source code
getcellcountsandmarginals_DE Source code
mglmOneGroup_notadjusted Source code
pvalueByBinoSupport Source code
pvalueByNegativeBinoSupport Source code
pvalueByNegativeBinoSupportApp Source code
pvalueDist Source code
pvalueSupport Source code
q2qnbinom_notadjusted Source code
twoestimators Source code

Files

NAMESPACE
data
data/arabidopsisM.RData
data/arabidopsisE.RData
R
R/Generalized_FDR_Estimators.r
R/Generalized_FDR_Estimators_ModularFuncs.r
R/MultipleTesting_HeteroDist.r
R/MultipleTesting_HeteroDist_ModFuncs.r
MD5
DESCRIPTION
man
man/GeneralizedEstimatorsGrouped.Rd
man/GeneralizedFDREstimators.Rd
man/arabidopsisE.Rd
man/arabidopsisM.Rd
fdrDiscreteNull documentation built on May 20, 2017, 1:41 a.m.