u3_fdr_ss: False Discovery Rates for Spatial Signals using Benjamini and...

In MixfMRI: Mixture fMRI Clustering Analysis

False Discovery Rates for Spatial Signals using Benjamini and Heller (2007)

Description

Compute q-values Benjamini and Heller's (2007) approach for controlling FDR for spatial signals.

Usage

  fdr.bh.p1(p, w = rep(1, length(p)), q = 0.05)
  fdr.bh.p2(p, w = rep(1, length(p)), q = 0.05)

Arguments

p	a p-value vector. No NA is allowed and all values are in [0, 1].
w	a weight vector for p-values.
q	a desired cutoff for adjusting p-values.

Details

These functions implement first two procedures in Benjamini and Heller (2007) for controlling FDR for spatial signals.

Value

Return the number of rejected hypotheses and all corresponding q-values for the input p-values.

Author(s)

Wei-Chen Chen.

References

Chen, W.-C. and Maitra, R. (2021) “A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging Studies”, arXiv:2102.03639.

See Also

qvalue().

Examples

library(MixfMRI, quietly = TRUE)
set.seed(1234)
da <- gendataset(phantom = shepp1fMRI, overlap = 0.01)
p <- da$pval[!is.na(da$pval)][1:100]
fdr.bh.p1(p)
fdr.bh.p2(p)

MixfMRI documentation built on Sept. 8, 2023, 5:06 p.m.