TTestOneSample(y, popmean=0, match=None, sub=None, ds=None, tail=0, samples=10000, pmin=None, tmin=None, tfce=False, tstart=None, tstop=None, parc=None, force_permutation=False, **criteria)¶
Mass-univariate one sample t-test
y (NDVar) – Dependent variable.
popmean (scalar) – Value to compare y against (default is 0).
match (None | categorial) – Combine data for these categories before testing.
sub (index) – Perform test with a subset of the data.
ds (Dataset) – If a Dataset is specified, all data-objects can be specified as names of Dataset variables
tail (0 | 1 | -1) – Which tail of the t-distribution to consider: 0: both (two-tailed); 1: upper tail (one-tailed); -1: lower tail (one-tailed).
samples (int) – Number of samples for permutation test (default 10,000).
pmin (None | scalar (0 < pmin < 1)) – Threshold for forming clusters: use a t-value equivalent to an uncorrected p-value.
tmin (scalar) – Threshold for forming clusters as t-value.
tfce (bool | scalar) – Use threshold-free cluster enhancement. Use a scalar to specify the step of TFCE levels (for
tfce is True, 0.1 is used).
tstart (scalar) – Start of the time window for the permutation test (default is the beginning of
tstop (scalar) – Stop of the time window for the permutation test (default is the end of
parc (str) – Collect permutation statistics for all regions of the parcellation of this dimension. For threshold-based test, the regions are disconnected.
force_permutation (bool) – Conduct permutations regardless of whether there are any clusters.
mintime (scalar) – Minimum duration for clusters (in seconds).
minsource (int) – Minimum number of sources per cluster.
difference (NDVar) – The difference value entering the test (
yif popmean is 0).
n (int) – Number of cases.
p (NDVar | None) – Map of p-values corrected for multiple comparison (or None if no correction was performed).
p_uncorrected (NDVar) – Map of p-values uncorrected for multiple comparison.
t (NDVar) – Map of t-values.
tfce_map (NDVar | None) – Map of the test statistic processed with the threshold-free cluster enhancement algorithm (or None if no TFCE was performed).
Information on the different permutation methods
Data points with zero variance are set to t=0.
Retrieve a specific cluster as NDVar
Compute a probability map
Find significant regions or clusters
Find peaks in a threshold-free cluster distribution
List with information about the test
Difference map masked by significance
Create a copy of the parameter map masked by significance