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- function [Q] = spm_P_clusterFDR(k,df,STAT,R,n,ui,Ps)
- % Return the corrected FDR q-value
- % FORMAT [Q] = spm_P_clusterFDR(k,df,STAT,R,n,ui,Ps)
- %
- % k - extent {RESELS}
- % df - [df{interest} df{residuals}]
- % STAT - Statistical field
- % 'Z' - Gaussian field
- % 'T' - T - field
- % 'X' - Chi squared field
- % 'F' - F - field
- % R - RESEL Count {defining search volume}
- % n - Conjunction number
- % ui - feature-inducing threshold
- % Ps - Vector of sorted (ascending) p-values
- % Q - FDR q-value
- %__________________________________________________________________________
- %
- % References
- %
- % J.R. Chumbley and K.J. Friston, "False discovery rate revisited: FDR and
- % topological inference using Gaussian random fields". NeuroImage,
- % 44(1):62-70, 2009.
- %
- % J.R. Chumbley, K.J. Worsley, G. Flandin and K.J. Friston, "Topological
- % FDR for NeuroImaging". Under revision.
- %__________________________________________________________________________
- % Copyright (C) 2009 Wellcome Trust Centre for Neuroimaging
- % Justin Chumbley & Guillaume Flandin
- % $Id: spm_P_clusterFDR.m 2764 2009-02-19 15:30:03Z guillaume $
- % Compute uncorrected p-values based on k using Random Field Theory
- %--------------------------------------------------------------------------
- [P, Z] = spm_P_RF(1, k, ui, df, STAT, R, n);
- % q value using the Benjamini & Hochberch False Discovery Rate procedure
- %--------------------------------------------------------------------------
- Q = spm_P_FDR(Z, df, 'P', n, Ps);
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