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iCAPs/packages/spm12/external/fieldtrip/statfun/ft_statfun_depsamplesFunivariate.m
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ft_statfun_depsamplesFunivariate.m
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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.fieldtriptoolbox.org
% 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/>.
%
% $Id$
% 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')
ft_error('P-values can only be calculated if the test statistics are calculated.');
end
if ~isfield(cfg,'uvar') || isempty(cfg.uvar)
ft_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)=sum(design(cfg.ivar,:)==condindx);
end
if sum(nuospercond)<size(design,2) || any(nuospercond~=nuospercond(1))
ft_error('Invalid specification of the design array.');
end
nunits = max(design(cfg.uvar,:));
dfdenom = nunits - ncontrasts;
if dfdenom<1
ft_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))
ft_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 squares
% meanYsub = mean(Ysub);
% SStot = sum((datonesmpl(:)-meanYsub).^2);
% SSsub = nconds * sum(sum((Ysub-meanYsub).^2));
% SSfac = nunits * sum(sum((Yfac-meanYsub).^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
dat = reshape(dat(:,poslabelsperunit),[nsmpls nunits nconds]);
Ysub = mean(dat,3);
Yfac = mean(dat,2);
meanYsub = mean(Ysub,2);
SStot = sum(sum((dat-meanYsub(:,ones(1,nunits),ones(1,nconds))).^2,3),2);
SSsub = nconds * sum((Ysub-meanYsub(:,ones(1,nunits))).^2,2);
SSfac = nunits * sum((Yfac-meanYsub(:,1,ones(1,nconds))).^2,3);
SSerr = SStot - SSsub - SSfac;
% mean sum of squares for factor levels
df = nconds - 1;
dfe = numel(dat(1,:,:)) - nunits - df;
MSfac = SSfac/df;
MSerr = SSerr/dfe;
s.stat = MSfac./MSerr; % F-statistic;
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
ft_error('For a dependent samples F-statistic, it does not make sense to calculate a left tail critical value.');
end
if cfg.tail==0
ft_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
ft_error('For a dependent samples F-statistic, it does not make sense to calculate a left tail p-value.');
end
if cfg.tail==0
ft_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
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