function [s,cfg] = ft_statfun_depsamplesFunivariate(cfg, dat, design)

% FT_STATFUN_DEPSAMPLESFUNIIVARIATE calculates the univariate repeated-mesures
% ANOVA on the biological data in dat (the dependent variable), using the
% information on the independent variable (ivar) in design.
%
% Use this function by calling one of the high-level statistics functions as
%   [stat] = ft_timelockstatistics(cfg, timelock1, timelock2, ...)
%   [stat] = ft_freqstatistics(cfg, freq1, freq2, ...)
%   [stat] = ft_sourcestatistics(cfg, source1, source2, ...)
% with the following configuration option
%   cfg.statistic = 'ft_statfun_depsamplesFunivariate'
% see FT_TIMELOCKSTATISTICS, FT_FREQSTATISTICS or FT_SOURCESTATISTICS for details.
%
% For low-level use, the external interface of this function has to be
%   [s,cfg] = ft_statfun_depsamplesFunivariate(cfg, dat, design);
% where
%   dat    contains the biological data, Nsamples x Nreplications
%   design contains the independent variable (ivar) and the unit-of-observation (uvar)
%          factor, Nfac x Nreplications
%
% Configuration options
%   cfg.computestat    = 'yes' or 'no', calculate the statistic (default='yes')
%   cfg.computecritval = 'yes' or 'no', calculate the critical values of the test statistics (default='no')
%   cfg.computeprob    = 'yes' or 'no', calculate the p-values (default='no')
%
% The following options are relevant if cfg.computecritval='yes' and/or
% cfg.computeprob='yes'.
%   cfg.alpha = critical alpha-level of the statistical test (default=0.05)
%   cfg.tail  = -1, 0, or 1, left, two-sided, or right (default=1)
%               cfg.tail in combination with cfg.computecritval='yes'
%               determines whether the critical value is computed at
%               quantile cfg.alpha (with cfg.tail=-1), at quantiles
%               cfg.alpha/2 and (1-cfg.alpha/2) (with cfg.tail=0), or at
%               quantile (1-cfg.alpha) (with cfg.tail=1). For the
%               Fstatistic only cfg.tail = 1 makes sense.
%
% Design specification
%   cfg.ivar  = row number of the design that contains the labels of the conditions that must be
%               compared (default=1). The labels range from 1 to the number of conditions.
%   cfg.uvar  = row number of design that contains the labels of the units-of-observation (subjects or trials)
%               (default=2). The labels are assumed to be integers ranging from 1 to
%               the number of units-of-observation.

% Copyright (C) 2014, Diego Lozano-Soldevilla
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
%    FieldTrip 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
%    (at your option) any later version.
%
%    FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_statfun_depsamplesFunivariate.m 9825 2014-09-22 15:19:53Z roboos $


% set defaults
if ~isfield(cfg, 'computestat'),       cfg.computestat='yes';     end;
if ~isfield(cfg, 'computecritval'),    cfg.computecritval='no';   end;
if ~isfield(cfg, 'computeprob'),       cfg.computeprob='no';      end;
if ~isfield(cfg, 'alpha'),             cfg.alpha=0.05;            end;
if ~isfield(cfg, 'tail'),              cfg.tail=1;                end;

nconds=length(unique(design(cfg.ivar,:)));
ncontrasts = nconds-1;

% perform some checks on the configuration
if strcmp(cfg.computeprob,'yes') & strcmp(cfg.computestat,'no')
  error('P-values can only be calculated if the test statistics are calculated.');
end;
if ~isfield(cfg,'uvar') || isempty(cfg.uvar)
  error('uvar must be specified for dependent samples statistics');
end

% perform some checks on the design
nuospercond=zeros(nconds,1);
for condindx=1:nconds
  nuospercond(condindx)=length(find(design(cfg.ivar,:)==condindx));
end;
if sum(nuospercond)<size(design,2) | nuospercond~=(nuospercond(1)*ones(nconds,1))
  error('Invalid specification of the design array.');
end;
nunits = max(design(cfg.uvar,:));
dfdenom = nunits - ncontrasts;
if dfdenom<1
  error('The data must contain more units-of-observation (usually subjects) than the number of contrasts.')
end;
nrepl=nunits*nconds;
if (nrepl~=sum(nuospercond)) | (nrepl~=size(dat,2))
  error('Invalid specification of the design array.');
end;
nsmpls = size(dat,1);

if strcmp(cfg.computestat,'yes')
  % compute the statistic
  % store the positions of the condition labels nunits-by-nconds array
  poslabelsperunit=zeros(nunits,nconds);
  for condindx=1:nconds
    poslabel=find(design(cfg.ivar,:)==condindx);
    [dum,i]=sort(design(cfg.uvar,poslabel),'ascend');
    poslabelsperunit(:,condindx)=poslabel(i);
  end;
  % reshape poslabelsperunit into a row vector that contains the
  % replications of the first condition on the first nunits positions,
  % the replications of the second condition on the second nunits
  % positions, etc.
  poslabelsperunit=reshape(poslabelsperunit,1,nrepl);
  s.stat=zeros(nsmpls,1);
  for smplindx=1:nsmpls
    datonesmpl=reshape(dat(smplindx,poslabelsperunit),nunits,nconds);
    
    Ysub = mean(datonesmpl,2);
    Yfac = mean(datonesmpl,1);
    
    % computing sums of squeares
    SStot = sum(sum((datonesmpl-mean(Ysub)).^2));
    SSsub = nconds * sum(sum((Ysub-mean(Ysub)).^2));
    SSfac = nunits * sum(sum((Yfac-mean(Ysub)).^2));
    SSerr = SStot - SSsub - SSfac;
    
    % mean sum of squares for factor levels
    df  = nconds - 1;
    dfe = size(datonesmpl(:),1)  - nunits - df;
    
    MSfac = SSfac/df;
    MSerr = SSerr/dfe;
    
    s.stat(smplindx) = MSfac/MSerr;% F-statistic
  end
end;

if strcmp(cfg.computecritval,'yes')
  % also compute the critical values
  s.dfnum = nconds - 1;
  s.dfdenom = nrepl - nunits - s.dfnum;
  if cfg.tail==-1
    error('For a dependent samples F-statistic, it does not make sense to calculate a left tail critical value.');
  end;
  if cfg.tail==0
    error('For a dependent samples F-statistic, it does not make sense to calculate a two-sided critical value.');
  end;
  if cfg.tail==1
    s.critval = finv(1-cfg.alpha,s.dfnum,s.dfdenom);
  end;
end

if strcmp(cfg.computeprob,'yes')
  % also compute the p-values
  s.dfnum = nconds - 1;
  s.dfdenom = nrepl - nunits - s.dfnum;
  if cfg.tail==-1
    error('For a dependent samples F-statistic, it does not make sense to calculate a left tail p-value.');
  end;
  if cfg.tail==0
    error('For a dependent samples F-statistic, it does not make sense to calculate a two-sided p-value.');
  end;
  if cfg.tail==1
    s.prob = 1-fcdf(s.stat,s.dfnum,s.dfdenom);
  end;
end
