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- function [P,p,Ec,Ek] = spm_P(c,k,Z,df,STAT,R,n,S)
- % Return the [un]corrected P value using unified EC theory
- % FORMAT [P,p,Ec,Ek] = spm_P(c,k,Z,df,STAT,R,n,S)
- %
- % c - cluster number
- % k - extent {RESELS}
- % Z - height {minimum over n values}
- % df - [df{interest} df{error}]
- % STAT - Statistical field
- % 'Z' - Gaussian field
- % 'T' - T - field
- % 'X' - Chi squared field
- % 'F' - F - field
- % 'P' - Posterior probability
- % R - RESEL Count {defining search volume}
- % n - number of component SPMs in conjunction
- % S - Voxel count
- %
- % P - corrected P value - P(C >= c | K >= k}
- % p - uncorrected P value
- % Ec - expected total number of clusters
- % Ek - expected total number of resels per cluster
- %
- %__________________________________________________________________________
- %
- % spm_P determines corrected and uncorrected p values, using the minimum
- % of different valid methods.
- %
- % See also: spm_P_RF, spm_P_Bonf
- %__________________________________________________________________________
- % Copyright (C) 2001-2011 Wellcome Trust Centre for Neuroimaging
- % Thomas Nichols
- % $Id: spm_P.m 4419 2011-08-03 18:42:35Z guillaume $
- if nargin < 8, S = []; end
- [P,p,Ec,Ek] = spm_P_RF(c,k,Z,df,STAT,R,n);
- % Compare with Bonferroni P value (if possible)
- %--------------------------------------------------------------------------
- if ~isempty(S) && (c == 1 && k == 0) && ~isequal(R, 1)
- P = min(P, spm_P_Bonf(Z,df,STAT,S,n));
- end
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