- mne.stats.f_mway_rm(data, factor_levels, effects='all', correction=False, return_pvals=True)[source]#
Compute M-way repeated measures ANOVA for fully balanced designs.
3D array where the first two dimensions are compliant with a subjects X conditions scheme where the first factor repeats slowest:
A1B1 A1B2 A2B1 A2B2 subject 1 1.34 2.53 0.97 1.74 subject ... .... .... .... .... subject k 2.45 7.90 3.09 4.76
The last dimensions is thought to carry the observations for mass univariate analysis.
The number of levels per factor.
A string denoting the effect to be returned. The following mapping is currently supported (example with 2 factors):
'A': main effect of A
'B': main effect of B
'A:B': interaction effect
'A+B': both main effects
'A*B': all three effects
'all': all effects (equals ‘A*B’ in a 2 way design)
If list, effect names are used:
['A', 'B', 'A:B'].
The correction method to be employed if one factor has more than two levels. If True, sphericity correction using the Greenhouse-Geisser method will be applied.
If True, return p-values corresponding to F-values.
New in version 0.10.
Mass-univariate twoway repeated measures ANOVA on single trial power
Repeated measures ANOVA on source data with spatio-temporal clustering