sgof: An R Package for Multiple Testing Problems

In this paper we present a new R package called sgof for multiple hypothesis testing. The principal aim of this package is to implement SGoF-type multiple testing methods, known to be more powerful than the classical false discovery rate (FDR) and family-wise error rate (FWER) based methods in certain situations, particularly when the number of tests is large. This package includes Bi nomial and Conservative SGoF and the Bayesian and Beta-Binomial SGoF multiple testing procedures, which are adaptations of the original SGoF method to the Bayesian setting and to possibly correlated tests, respectively. The sgof package also implements the Benjamini-Hochberg and Benjamini-Yekutieli FDR controlling procedures. For each method the package provides (among other things) the number of rejected null hypotheses, estimation of the corresponding FDR, and the set of adjusted p values. Some automatic plots of interest are implemented too. Two real data examples are used to illustrate how sgof works.

Irene Castro-Conde , Jacobo de Uña-Álvarez

CRAN packages used

sgof, mutoss, multcomp

CRAN Task Views implied by cited packages

ClinicalTrials, SocialSciences, Survival

Bioconductor packages used

qvalue, HybridMTest, multtest


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For attribution, please cite this work as

Castro-Conde & Uña-Álvarez, "The R Journal: sgof: An R Package for Multiple Testing Problems", The R Journal, 2014

BibTeX citation

  author = {Castro-Conde, Irene and Uña-Álvarez, Jacobo de},
  title = {The R Journal: sgof: An R Package for Multiple Testing Problems},
  journal = {The R Journal},
  year = {2014},
  note = {},
  doi = {10.32614/RJ-2014-027},
  volume = {6},
  issue = {2},
  issn = {2073-4859},
  pages = {96-113}