eelbrain.test.pairwise(y:Union[eelbrain._data_obj.Var, str], x:Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str], match:Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str]=None, sub:Union[eelbrain._data_obj.Var, numpy.ndarray, str]=None, ds:eelbrain._data_obj.Dataset=None, par:bool=True, corr:Union[NoneType, str]='Hochberg', trend:Union[bool, str]=True, title:str='{desc}', mirror:bool=False)

Pairwise comparison table

y : Var

Dependent measure.

x : categorial

Categories to compare.

match : None | Factor

Repeated measures factor.

sub : None | index-array

Perform tests with a subset of the data.

ds : Dataset

If a Dataset is given, all data-objects can be specified as names of Dataset variables.

par : bool

Use parametric test for pairwise comparisons (use non-parametric tests if False).

corr : None | ‘hochberg’ | ‘bonferroni’ | ‘holm’

Method for multiple comparison correction.

trend : None | str

Marker for a trend in pairwise comparisons.

title : str

Title for the table.

mirror : bool

Redundant table including all row/column combinations.

table : FMText Table

Table with results.