BH_procedure: Multiple statistical hypothesis testing with the...
In flatness: Indices and Tests for Assessing the Flatness of (Rank) Histograms

Description Usage Arguments Details Value References

This function applies the procedure described in Benjamini & Hochberg (1995) for controlling the False Discovery Rate in multiple statistical hypothesis testing.

1	BH_procedure(pvalues, alpha = 0.01, ...)

`pvalues`	a vector of p-values, with length N
`alpha`	a numeric between 0 and 1
`...`	same arguments as in function base::sort, except "index.return" which is set to TRUE.

The procedure works as follows. Let N p-values p_i (with i=1,…,N) and a significance level α. The decision threshold is p_k where k = max(i;p_{(i)} <= α \frac{i}{N}) where p_{(i)} are the sorted p-values p_i. For every test with p_i ≤ p_k, the null hypothesis is rejected. By convention p_{(0)} = 0.

A named listed with entries:

H0: a logical vector of length N. The nth entry has value TRUE if the null hypothesis associated with the nth p-value is not rejected and FALSE otherwise.
pk: the decision threshold. A p-value under this threshold leads to rejection of the associated null hypothesis.
alpha: the chosen significance level
pvalues: the vector of p-values provided to apply the Benjamini-Hochberg procedure.

Benjamini, Y., & 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(1), 289-300. doi:https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

flatness documentation built on June 29, 2021, 9:08 a.m.