spm_P.m

function [P,p,Ec,Ek] = spm_P(c,k,Z,df,STAT,R,n,S)
% Returns the [un]corrected P value using unifed 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 the individual methods for details
%
%     spm_P_RF
%     spm_P_Bonf
%
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Thomas Nichols
% $Id: spm_P.m 3824 2010-04-19 19:23:35Z karl $


% set global var NOBONF to 1 to turn off Bonferroni
%--------------------------------------------------------------------------
global NOBONF; if ~isempty(NOBONF) && NOBONF, S = []; end

if nargin < 8, S = []; end

[P,p,Ec,Ek] = spm_P_RF(c,k,Z,df,STAT,R,n);

% Use lower Bonferroni P value (if possible)
%==========================================================================
if ~isempty(S) && (c == 1 && k == 0) && ~(length(R) == 1 && R == 1)
    P = min(P,spm_P_Bonf(Z,df,STAT,S,n));
end