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ft_statfun_indepsamplesT.m
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ft_statfun_indepsamplesT.m
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function [s, cfg] = ft_statfun_indepsamplesT(cfg, dat, design)
% FT_STATFUN_INDEPSAMPLEST calculates the independent samples T-statistic 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_indepsamplesT'
%
% 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 = independent variable, row number of the design that contains the labels of the conditions to be compared (default=1)
%
% The labels for the independent variable should be specified as the number 1 and 2.
%
% 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);
% perform some checks on the configuration
if strcmp(cfg.computeprob,'yes') && strcmp(cfg.computestat,'no')
% probabilities can only be calculated if the test statistics are calculated
cfg.computestat = 'yes';
end
if isfield(cfg,'uvar') && ~isempty(cfg.uvar)
ft_error('cfg.uvar should not exist for an independent samples statistic');
end
% perform some checks on the design and data
sel1 = design(cfg.ivar,:)==1;
sel2 = design(cfg.ivar,:)==2;
nreplc1 = sum(~isnan(dat(:,sel1)), 2);
nreplc2 = sum(~isnan(dat(:,sel2)), 2);
nrepl = nreplc1 + nreplc2;
hasnans1 = any(nreplc1<sum(sel1));
hasnans2 = any(nreplc2<sum(sel2));
if any(nrepl<size(design,2))
ft_warning('Not all replications are used for the computation of the statistic.');
end
df = nrepl - 2;
if strcmp(cfg.computestat, 'yes')
% compute the statistic, use nanmean only if necessary
if hasnans1
avg1 = nanmean(dat(:,sel1), 2);
var1 = nanvar(dat(:,sel1), 0, 2);
else
avg1 = mean(dat(:,sel1), 2);
var1 = var(dat(:,sel1), 0, 2);
end
if hasnans2
avg2 = nanmean(dat(:,sel2), 2);
var2 = nanvar(dat(:,sel2), 0, 2);
else
avg2 = mean(dat(:,sel2), 2);
var2 = var(dat(:,sel2), 0, 2);
end
% compute the combined variance
varc = ((nreplc1-1).*var1 + (nreplc2-1).*var2) .* (1./nreplc1 + 1./nreplc2) ./ df;
% in the case of non-equal trial lengths, and TFRs as input-data, nreplc are
% vectors with different values. When the trial lengths are equal, and the
% input is a TFR, nreplc are vectors with either zeros (all trials contain nan
% meaning that t_ftimwin did not fit around data), or the number of trials
s.stat = (avg1 - avg2)./sqrt(varc);
end
if strcmp(cfg.computecritval,'yes')
% also compute the critical values
s.df = df;
if all(df==df(1)), df = df(1); end
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