palm_datapval.m

function pvals = palm_datapval(G,Gvals,rev)
% Compute the p-values for a set of statistics G, taking
% as reference a set of observed values for G, from which
% the empirical cumulative distribution function (cdf) is
% generated, or using a custom cdf.
%
% Usage:
% pvals = palm_datapval(G,Gvals,rev)
%
% Inputs:
% G     : Array of statistics to be converted to p-values
% Gvals : A Mx1 vector of observed values for the same statistic
%         from which the empirical cdf is build and p-values
%         obtained. It doesn't have to be sorted.
% rev   : If true, indicates that the smallest values in G and
%         Gvals, rather than the largest, are the most significant.
%
% Output:
% pvals : P-values.
%
% This function is a simplification of the much more generic
% 'cdftool.m' so that only the 'data' option is retained.
% To increase speed, there is no argument checking.
%
% _____________________________________
% Anderson M. Winkler
% FMRIB / Univ. of Oxford
% Jul/2012 (1st version)
% Jan/2014 (this version)
% http://brainder.org

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% PALM -- Permutation Analysis of Linear Models
% Copyright (C) 2015 Anderson M. Winkler
% 
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% any later version.
% 
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
% 
% You should have received a copy of the GNU General Public License
% along with this program.  If not, see <http://www.gnu.org/licenses/>.
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

if rev % if small G are significant
    
    % Sort the data and compute the empirical distribution
    [~,cdfG,distp] = palm_competitive(Gvals(:),'ascend',true);
    cdfG  = unique(cdfG);
    distp = unique(distp)./numel(Gvals);
    
    % Convert the data to p-values
    pvals = zeros(size(G));
    for g = 1:numel(cdfG)
        pvals(G >= cdfG(g)) = distp(g);
    end
    pvals(G > cdfG(end)) = 1;
    
else % if large G are significant (typical case)
    
    % Sort the data and compute the empirical distribution
    [~,cdfG,distp] = palm_competitive(Gvals(:),'descend',true);
    cdfG  = unique(cdfG);
    distp = flipud(unique(distp))./numel(Gvals);
    
    % Convert the data to p-values
    pvals = zeros(size(G));
    for g = numel(cdfG):-1:1
        pvals(G <= cdfG(g)) = distp(g);
    end
    pvals(G > cdfG(end)) = 0;
end