eelbrain.test.MannWhitneyU
- class eelbrain.test.MannWhitneyU(y, x, c1=None, c0=None, match=None, sub=None, data=None, tail=0, continuity=True)
Mann-Whitney U-test (non-parametric independent measures test)
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 (Var) – Dependent variable. Alternatively, the first of two variables that are compared.
x (categorial) – Model containing the cells which should be compared. Alternatively, the second of two varaibles that are compared.
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) – Units within which measurements are related (e.g. ‘subject’ in a within-subject comparison). If match is unspecified, it is assumed that
y
andx
are two measurements with matched cases.sub (index-array) – 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.
tail (0 | 1 | -1) – Which tail of the t-distribution to consider: 0: both (two-tailed, default); 1: upper tail (one-tailed); -1: lower tail (one-tailed).
continuity (bool) – Continuity correction (default
True
).
- Variables:
See also
TTestRelated
parametric alternative
Notes
Based on
scipy.stats.mannwhitneyu()
.