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ft_statfun_depsamplesregrT.m
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ft_statfun_depsamplesregrT.m
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function [s, cfg] = ft_statfun_depsamplesregrT(cfg, dat, design)
% FT_STATFUN_DEPSAMPLESREGRT calculates independent samples regression coefficient
% t-statistics on the biological data (the dependent variable), using the information
% on the independent variable (predictor) in the 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_depsamplesregrT'
%
% You can specify the following 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)
%
% The experimental design is specified as:
% cfg.ivar = row number of the design that contains the independent variable, i.e. the predictor (default=1)
% cfg.uvar = unit variable, row number of design that contains the labels of the units-of-observation, i.e. subjects or trials (default=2)
%
% The labels for the unit of observation should be integers ranging from 1 to the
% total number of observations (subjects or trials).
%
% See also FT_TIMELOCKSTATISTICS, FT_FREQSTATISTICS or FT_SOURCESTATISTICS
% Copyright (C) 2006, Eric Maris
%
% 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 the defaults
cfg.computestat = ft_getopt(cfg, 'computestat', 'yes');
cfg.computecritval = ft_getopt(cfg, 'computecritval', 'no');
cfg.computeprob = ft_getopt(cfg, 'computeprob', 'no');
cfg.alpha = ft_getopt(cfg, 'alpha', 0.05);
cfg.tail = ft_getopt(cfg, 'tail', 1);
cfg.ivar = ft_getopt(cfg, 'ivar', 1);
cfg.uvar = ft_getopt(cfg, 'uvar', 2);
% 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
if ~isempty(cfg.cvar)
condlabels = unique(design(cfg.cvar,:));
nblocks = length(condlabels);
else
nblocks = 1;
end
nunits = max(design(cfg.uvar,:));
df = nunits - 1;
if nunits<2
ft_error('The data must contain at least two units-of-observation (usually subjects).')
end
if strcmp(cfg.computestat,'yes')
% compute the statistic
regrweights=zeros(size(dat,1),nunits);
for indx=1:nunits
unitselvec=find(design(cfg.uvar,:)==indx);
indvar=design(cfg.ivar,unitselvec);
if isempty(cfg.cvar)
designmat=[ones(1,length(indvar));indvar];
else
designmat=zeros((nblocks+1),length(indvar));
for blockindx=1:nblocks
blockselvec=find(design(cfg.cvar,unitselved)==condlabels(blockindx));
designmat(blockindx,blockselvec)=1;
end
designmat((nblocks+1),:)=indvar;
end
coeff=(designmat*designmat')\(designmat*dat(:,unitselvec)');
regrweights(:,indx)=coeff((nblocks+1),:)';
end
avgw=mean(regrweights,2);
varw=var(regrweights,0,2);
s.stat=sqrt(nunits)*avgw./sqrt(varw);
end
if strcmp(cfg.computecritval,'yes')
% also compute the critical values
s.df = df;
if cfg.tail==-1
s.critval = tinv(cfg.alpha,df);
elseif cfg.tail==0
s.critval = [tinv(cfg.alpha/2,df),tinv(1-cfg.alpha/2,df)];
elseif cfg.tail==1
s.critval = tinv(1-cfg.alpha,df);
end
end
if strcmp(cfg.computeprob,'yes')
% also compute the p-values
s.df = df;
if cfg.tail==-1
s.prob = tcdf(s.stat,s.df);
elseif cfg.tail==0
s.prob = 2*tcdf(-abs(s.stat),s.df);
elseif cfg.tail==1
s.prob = 1-tcdf(s.stat,s.df);
end
end