eelbrain.testnd.TTestIndependent
 class eelbrain.testnd.TTestIndependent(y, x, c1=None, c0=None, 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)
Massunivariate independent samples ttest
The test data can be specified in two forms:
In long form, with
y
supplying the data,x
specifying condition for each case.With
y
andx
supplying data for the two conditions.
 Parameters
y (NDVar) – Dependent variable.
x (categorial  NDVar) – Model containing the cells which should be compared, or NDVar to which
y
should be compared. In the latter case, the next three parameters are ignored.c1 (str  tuple  None) – Test condition (cell of
x
).c1
andc0
can be omitted ifx
only contains two cells, in which case cells will be used in alphabetical order.match (categorial) – Combine cases with the same cell on
x % match
.sub (index) – Perform the test with a subset of the data.
ds (Dataset) – If a Dataset is specified, all dataobjects can be specified as names of Dataset variables.
tail (0  1  1) – Which tail of the tdistribution to consider: 0: both (twotailed); 1: upper tail (onetailed); 1: lower tail (onetailed).
samples (int) – Number of samples for permutation test (default 10,000).
pmin (None  scalar (0 < pmin < 1)) – Threshold p value for forming clusters. None for thresholdfree cluster enhancement.
tmin (scalar) – Threshold for forming clusters as tvalue.
tfce (bool  scalar) – Use thresholdfree 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
y
).tstop (scalar) – Stop of the time window for the permutation test (default is the end of
y
).parc (str) – Collect permutation statistics for all regions of the parcellation of this dimension. For thresholdbased 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.
 Variables
c1_mean (NDVar) – Mean in the c1 condition.
c0_mean (NDVar) – Mean in the c0 condition.
clusters (None  Dataset) – For clusterbased tests, a table of all clusters. Otherwise a table of all significant regions (or
None
if permutations were omitted). See also thefind_clusters()
method.difference (NDVar) – Difference between the mean in condition c1 and condition c0.
p (NDVar  None) – Map of pvalues corrected for multiple comparison (or None if no correction was performed).
p_uncorrected (NDVar) – Map of pvalues uncorrected for multiple comparison.
t (NDVar) – Map of tvalues.
tfce_map (NDVar  None) – Map of the test statistic processed with the thresholdfree cluster enhancement algorithm (or None if no TFCE was performed).
See also
testnd
Information on the different permutation methods
Notes
Cases with zero variance are set to t=0.
Methods

Retrieve a specific cluster as NDVar 

Compute a probability map 

Find significant regions or clusters 
Find peaks in a thresholdfree cluster distribution 


List with information about the test 

Difference map masked by significance 

Statistical parameter map masked by significance 