.. _stats_fdr:
******************************************************
**False Discovery Rate (FDR)**
******************************************************
.. contents:: :local:
*These are notes by B. Douglas Ward, written in the Good Ol' Days.*
Program 3dFDR
=========================
Purpose
---------
Program ``3dFDR`` implements the False Discovery Rate (FDR) algorithm
for thresholding of voxelwise statistics. Instead of controlling for
*alpha* (the probabilty of a false positive anywhere in the volume),
the FDR algorithm controls for the proportion of false positives
relative to the number of detections.
Program input consists of a dataset containing one (or more)
statistical sub-bricks. Output consists of a bucket dataset with one
sub-brick for each input sub-brick. For non-statistical input
sub-bricks, the output sub-brick is a copy of the input. However,
statistical input sub-bricks are replaced by the corresponding FDR
values, as follows:
For each voxel, the minimum value of *q* is determined such that
.. math:: E(FDR) \leq q
leads to rejection of the null hypothesis in that voxel. Only voxels
inside the user specified mask (optional) will be considered. These
*q*\ -values are them mapped to *z*\ -scores for compatability with
the AFNI statistical threshold display, i.e.,
.. math:: {\rm input~stat} \rightarrow p \mbox{-value} \rightarrow
{\rm FDR}~q \mbox{-value} \rightarrow {\rm FDR}~z
\mbox{-score}
These calculations are performed independently for each statistical
sub-brick.
*... [end of excerpt; see below for the full document]*
\.\.\. Full Enlightenment
=========================
| For continued enjoyment of this topic, please see the complete
document here:
| `3dFDR.pdf
`_