eelbrain.table.repmeas

eelbrain.table.repmeas(y, x, match, sub=None, data=None)

Create a repeated-measures table

Parameters:

  • y (NDVar | str | Model | Var | Factor | Interaction | NestedEffect) – Dependent variable (can be model with several dependents).

  • x (Factor | Interaction | NestedEffect | str) – Model defining the cells that should be restructured into variables.

  • match (categorial) – Model identifying the source of the measurement across repetitions, i.e. the model that should be retained.

  • sub (Var | ndarray | str) – boolean array specifying which values to include (generate e.g. with ‘sub=T==[1,2]’)

  • data (Dataset) – If a Dataset is specified other arguments can be str instead of data-objects and will be retrieved from ds.

Returns:

rm_table – Repeated measures table. Entries for cells of x correspond to the data in y on these levels of x (if cell names are not valid Dataset keys they are modified).

Return type:

Dataset

Examples

Generate test data in long format:

>>> ds = Dataset()
>>> data['y'] = Var([1,2,3,5,6,4])
>>> data['x'] = Factor('aaabbb')
>>> data['rm'] = Factor('123231', random=True)
>>> print(data)
y   x   rm
----------
1   a   1
2   a   2
3   a   3
5   b   2
6   b   3
4   b   1

Compute difference between two conditions:

>>> ds_rm = table.repmeas('y', 'x', 'rm', data=data)
>>> print(ds_rm)
rm   a   b
----------
1    1   4
2    2   5
3    3   6
>>> ds_rm['difference'] = ds_rm.eval("b - a")
>>> print(ds_rm)
rm   a   b   difference
-----------------------
1    1   4   3
2    2   5   3
3    3   6   3