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)

Mass-univariate Pearson correlation significance test

  • 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 y).

  • tstop (scalar) – Stop of the time window for the permutation test (default is the end of y).

  • 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 None if permutations were omitted). See also the find_clusters() method.

  • 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).

See also


Information on the different permutation methods



Retrieve a specific cluster as NDVar


Compute a probability map

find_clusters([pmin, maps])

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