BatchBH.RdImplements the BatchBH algorithm for online FDR control, as presented by Zrnic et al. (2020).
BatchBH(d, alpha = 0.05, gammai, display_progress = FALSE)A dataframe with three columns: identifiers (`id'), batch numbers (`batch') and p-values (`pval').
Overall significance level of the FDR procedure, the default is 0.05.
Optional vector of \(\gamma_i\). A default is provided with \(\gamma_j\) proportional to \(1/j^(1.6)\).
Logical. If TRUE prints out a progress bar for the algorithm runtime.
A dataframe with the original data d and the
indicator function of discoveries R. Hypothesis \(i\) is rejected
if the \(i\)-th p-value within the \(t\)-th batch is less than or equal
to \((r/n)\alpha_t\), where \(r\) is the rank of the \(i\)-th p-value
within an ordered set and \(n\) is the total number of hypotheses within
the \(t\)-th batch. If hypothesis \(i\) is rejected, R[i] = 1
(otherwise R[i] = 0).
The function takes as its input a dataframe with three columns: identifiers (`id'), batch numbers (`batch') and p-values (`pval').
The BatchBH algorithm controls the FDR when the p-values in a batch are independent, and independent across batches. Given an overall significance level \(\alpha\), we choose a sequence of non-negative numbers \(\gamma_i\) such that they sum to 1. The algorithm runs the Benjamini-Hochberg procedure on each batch, where the values of the adjusted significance thresholds \(\alpha_{t+1}\) depend on the number of previous discoveries.
Further details of the BatchBH algorithm can be found in Zrnic et al. (2020).
Zrnic, T., Jiang D., Ramdas A. and Jordan M. (2020). The Power of Batching in Multiple Hypothesis Testing. International Conference on Artificial Intelligence and Statistics, 3806-3815.
sample.df <- data.frame(
id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902',
'C38292', 'A30619', 'D46627', 'E29198', 'A41418',
'D51456', 'C88669', 'E03673', 'A63155', 'B66033'),
pval = c(2.90e-08, 0.06743, 0.01514, 0.08174, 0.00171,
3.60e-05, 0.79149, 0.27201, 0.28295, 7.59e-08,
0.69274, 0.30443, 0.00136, 0.72342, 0.54757),
batch = c(rep(1,5), rep(2,6), rep(3,4)))
BatchBH(sample.df)
#> id pval batch R alphai
#> 1 A15432 2.9000e-08 1 1 0.021874508
#> 2 B90969 6.7430e-02 1 0 0.021874508
#> 3 C18705 1.5140e-02 1 0 0.021874508
#> 4 B49731 8.1740e-02 1 0 0.021874508
#> 5 E99902 1.7100e-03 1 1 0.021874508
#> 6 C38292 3.6000e-05 2 1 0.009621196
#> 7 A30619 7.9149e-01 2 0 0.009621196
#> 8 D46627 2.7201e-01 2 0 0.009621196
#> 9 E29198 2.8295e-01 2 0 0.009621196
#> 10 A41418 7.5900e-08 2 1 0.009621196
#> 11 D51456 6.9274e-01 2 0 0.009621196
#> 12 C88669 3.0443e-01 3 0 0.025012888
#> 13 E03673 1.3600e-03 3 1 0.025012888
#> 14 A63155 7.2342e-01 3 0 0.025012888
#> 15 B66033 5.4757e-01 3 0 0.025012888