FT_STATFUN_POOLEDT

Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_statfun_pooledT”.

FT_STATFUN_POOLEDT computes the pooled t-value over a number of
replications. The idea is that you compute a contrast between two
conditions per subject The t-values are pooled over subjects and
compared against the pooled pseudo-values. Since according to H0
the expected t-value for each subject value is zero, the difference
between the pooled t-value and the pseudo-value (which is set to
zero) is a fixed-effects statistic.

The computation of the difference between pooled t-values can be
repeated after randomly permuting the t-values and pseudo-values
within the subjects. Each random permutation gives you an estimate
of the difference. The random permutations build up a randomization
distributin, against which you can compare the observed pooled
t-values.

The statistical inference based on the comparison of the observed
pooled t-values with the randomization distribution is not a
fixed-effect statistic, one or a few outlier will cause the
randomization distribution to broaden and result in the conclusion
of "not significant".

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_pooledT'

Configuration options that are relevant for this function are
cfg.ivar      = number, index into the design matrix with the independent variable

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_pooledT(cfg, dat, design);
where
dat    contains the biological data, Nsamples x Nreplications
dat must contain fourier representations.
design contains the independent variable (ivar), Nreplications x Nvar