Given the proportion pi0 of tests with a true null, find the p-value threshold that results in a desired FDR and average power.
alpha.power.fdr(fdr, pwr, pi0, method = "HH")The fixed p-value threshold for multiple testing procedure
desired FDR (scalar numeric)
desired average power (scalar numeric)
the proportion of tests with a true null hypothesis
method to estimate proportion pi0 of tests with true null, including: "HH" (p-value histogram height) , "HM" (p-value histogram mean), "BH" (Benjamini & Hochberg 1995), "Jung" (Jung 2005)
To get the fixed p-value threshold for multiple testing procedure, 4 approximation methods are provided, they are Benjamini & Hochberg procedure (1995), Jung's formula (2005), method of using p-value histogram height (HH) and method of using p-value histogram mean (HM). For last two methods' details, see Ni Y, Onar-Thomas A, Pounds S. "Computing Power and Sample Size for the False Discovery Rate in Multiple Applications"
Pounds S and Cheng C, "Sample size determination for the false discovery rate." Bioinformatics 21.23 (2005): 4263-4271.
Gadbury GL, et al. (2004) Power and sample size estimation in high dimensional biology. Statistical Methods in Medical Research 13(4):325-38.
Jung,Sin-Ho."Sample size for FDR-control in microarray data analysis." Bioinformatics 21.14 (2005): 3097-3104.
Ni Y, Seffernick A, Onar-Thomas A, Pounds S. "Computing Power and Sample Size for the False Discovery Rate in Multiple Applications", Manuscript.
alpha.power.fdr(fdr = 0.1, pwr = 0.9, pi0=0.9, method = "HH")
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