- class eelbrain.testnd.Correlation(y, x, norm=None, sub=None, data=None, samples=10000, pmin=None, rmin=None, tfce=False, tstart=None, tstop=None, match=None, parc=None, **criteria)
y (NDVar) – Dependent variable.
x (continuous) – The continuous predictor variable.
norm (None | categorial) – Categories in which to normalize (z-score) x.
sub (index) – Perform the test with a subset of the data.
data (Dataset) – If a Dataset is specified, all data-objects can be specified as names of Dataset variables.
samples (int) – Number of samples for permutation test (default 10,000).
pmin (None | scalar (0 < pmin < 1)) – Threshold for forming clusters: use an r-value equivalent to an uncorrected p-value.
rmin (None | scalar) – Threshold for forming clusters.
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
match (None | categorial) – When permuting data, only shuffle the cases within the categories of match.
parc (str) – Collect permutation statistics for all regions of the parcellation of this dimension. For threshold-based test, the regions are disconnected.
mintime (scalar) – Minimum duration for clusters (in seconds).
minsource (int) – Minimum number of sources per cluster.
clusters (None | Dataset) – For cluster-based tests, a table of all clusters. Otherwise a table of all significant regions (or
Noneif permutations were omitted). See also the
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.
r (NDVar) – Map of correlation values (with threshold contours).
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
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
Statistical parameter map masked by significance