eelbrain.testnd.ANOVA¶

class
eelbrain.testnd.
ANOVA
(y, x, sub=None, ds=None, samples=10000, pmin=None, fmin=None, tfce=False, tstart=None, tstop=None, match=None, parc=None, force_permutation=False, **criteria)¶ Massunivariate ANOVA
 Parameters
y (NDVar) – Dependent variable.
x (Model) – Independent variables.
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.
samples (int) – Number of samples for permutation test (default 10,000).
pmin (None  scalar (0 < pmin < 1)) – Threshold for forming clusters: use an fvalue equivalent to an uncorrected pvalue.
fmin (scalar) – Threshold for forming clusters as fvalue.
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 (categorial  False) – When permuting data, only shuffle the cases within the categories of match. By default,
match
is determined automatically based on the random efects structure ofx
.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
effects (tuple of str) – Names of the tested effects, in the same order as in other attributes.
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.f (list of NDVar) – Maps of F values.
p (list of NDVar  None) – Maps of pvalues corrected for multiple comparison (or None if no correction was performed).
p_uncorrected (list of NDVar) – Maps of pvalues uncorrected for multiple comparison.
tfce_maps (list of NDVar  None) – Maps 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
Examples
For information on model specification see the univariate
ANOVA
examples.
Methods¶

Retrieve a specific cluster as NDVar 

Compute a probability map 

Find significant regions or clusters 
Find peaks in a TFCE distribution 


List with information about the test 

Create a copy of the parameter map masked by significance 

Table listing all effects and corresponding smallest pvalues 