sgof-package: Multiple hypothesis testing

In sgof: Multiple Hypothesis Testing

Multiple hypothesis testing

Description

This package implements seven different methods for multiple testing problems. The Benjamini and Hochberg (1995) false discovery rate controlling procedure and its modification for dependent tests Benjamini and Yekutieli (2001), the method called Binomial SGoF proposed in Carvajal Rodríguez et al. (2009) and its conservative and bayesian versions called Conservative SGoF (de Uña Álvarez, 2011) and Bayesian SGoF (Castro Conde and de Uña Álvarez, 2013 13/06), respectively, and the BB-SGoF (Beta-Binomial SGoF, de Uña Álvarez, 2012) and Discrete SGoF (Castro Conde et al., 2015) procedures which are adaptations of SGoF method for possibly correlated tests and for discrete tests, respectively. Number of rejections, FDR and adjusted p-values are computed among other things.

Details

This package incorporates the functions BH,BY, SGoF, Binomial.SGoF, Bayesian.SGoF, Discrete.SGoF and BBSGoF, which call the methods aforementioned. For a complete list of functions, use library(help="sgof").

Author(s)

Irene Castro Conde and Jacobo de Uña Álvarez

Maintainer:Irene Castro Conde [e-mail]

References

Benjamini Y and Hochberg Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289–300.

Benjamini Y and Yekutieli D (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165–-1188.

Carvajal Rodríguez A, de Uña Álvarez J and Rolán Álvarez E (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics 10:209.

Castro Conde I and de Uña Álvarez J. Power, FDR and conservativeness of BB-SGoF method. Computational Statistics; Volume 30, Issue 4, pp 1143-1161 DOI: 10.1007/s00180-015-0553-2.

Castro Conde I and de Uña Álvarez J (2015). Adjusted p-values for SGoF multiple test procedure. Biometrical Journal; 57(1): 108-122. DOI: 10.1002/bimj.201300238

Castro Conde I, Döhler S and de Uña Álvarez J (2015). An extended sequential goodness-of-fit multiple testing method for discrete data. Statistical Methods in Medical Research. doi: 10.1177/0962280215597580.

Castro Conde I and de Uña Álvarez J (2014). sgof: An R package for multiple testing problems. The R Journal; Vol. 6/2 December: 96-113.

Castro Conde I and de Uña Álvarez J (2013). SGoF multitesting method under the Bayesian paradigm. Discussion Papers in Statistics and Operation Research. Report 13/06. Statistics and OR Department. University of Vigo.

Dalmasso C, Broet P and Moreau T (2005). A simple procedure for estimating the false discovery rate. Bioinformatics 21:660–668

de Uña Álvarez J (2011). On the statistical properties of SGoF multitesting method. Statistical Applications in Genetics and Molecular Biology, Vol. 10, Iss. 1, Article 18.

de Uña Álvarez J (2012). The Beta-Binomial SGoF method for multiple dependent tests. Statistical Applications in Genetics and Molecular Biology, Vol. 11, Iss. 3, Article 14.

Durand G, Junge F (2021). DiscreteFDR: Multiple Testing Procedures with Adaptation for Discrete Tests. R package version 1.3.6

Hong Y. (2013). On computing the distribution functions for the Poisson binomial distribution. Computational Statistics and Data Analysis 59, 41-51.

Hong Y. (2019). poibin: The Poisson Binomial Distribution. R package version 1.4

Pounds, S. and C. Cheng (2006). Robust estimation of the false discovery rate. Bioinformatics 22 (16), 1979-1987.

sgof documentation built on Sept. 8, 2023, 5:34 p.m.