spm_P_clusterFDR.m
e5f5005bb2efmaster

spm_P_clusterFDR.m
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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);