class eelbrain.test.WilcoxonSignedRank(y, x, c1=None, c0=None, match=None, sub=None, data=None, tail=0, zero_method='wilcox', correction=False)

Wilcoxon signed-rank test (non-parametric related 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 and match determining which cases are related.

  • In wide/repeated measures form, with y and x both supplying data with matching case order.

  • 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 and c0 can be omitted if x only contains two cells, in which case cells will be used in alphabetical order.

  • c0 (str | tuple | None) – Control condition (cell of x).

  • 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 and x 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).

  • zero_method (str) – How to handle zero differences (see scipy.stats.wilcoxon()).

  • correction (bool) – Continuity correction (default False).

  • w (float) – Rank sum statistic.

  • p (float) – P-value.

  • tail (0 | 1 | -1) – Tailedness of the p value.

  • difference (Var) – Difference values.

  • c1_mean (float) – Mean of condition c1.

  • c0_mean (float) – Mean of condition c0.

See also


parametric alternative


Based on scipy.stats.wilcoxon().