eelbrain.testnd.Correlation
 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)
Massunivariate Pearson correlation significance test
 Parameters:
y (NDVar) – Dependent variable.
x (continuous) – The continuous predictor variable.
norm (None  categorial) – Categories in which to normalize (zscore) x.
sub (index) – Perform the test with a subset of the data.
data (Dataset) – If a Dataset is specified, all dataobjects 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 rvalue equivalent to an uncorrected pvalue.
rmin (None  scalar) – Threshold for forming clusters.
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
).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 thresholdbased test, the regions are disconnected.
mintime (scalar) – Minimum duration for clusters (in seconds).
minsource (int) – Minimum number of sources per cluster.
 Variables:
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.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.
r (NDVar) – Map of correlation values (with threshold contours).
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
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 

Statistical parameter map masked by significance 