pingouin.mixed_anova(data=None, dv=None, within=None, subject=None, between=None, correction='auto', export_filename=None)[source]

Mixed-design (split-plot) ANOVA.


DataFrame. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed.


Name of column containing the dependant variable.


Name of column containing the within factor.


Name of column containing the subject identifier.


Name of column containing the between factor.

correctionstring or boolean

If True, return Greenhouse-Geisser corrected p-value. If ‘auto’ (default), compute Mauchly’s test of sphericity to determine whether the p-values needs to be corrected.


Filename (without extension) for the output file. If None, do not export the table. By default, the file will be created in the current python console directory. To change that, specify the filename with full path.


ANOVA summary

'Source' : Names of the factor considered
'ddof1' : Degrees of freedom (numerator)
'ddof2' : Degrees of freedom (denominator)
'F' : F-values
'p-unc' : Uncorrected p-values
'np2' : Partial eta-square effect sizes
'eps' : Greenhouse-Geisser epsilon factor ( = index of sphericity)
'p-GG-corr' : Greenhouse-Geisser corrected p-values
'W-spher' : Sphericity test statistic
'p-spher' : p-value of the sphericity test
'sphericity' : sphericity of the data (boolean)

See also


One-way and N-way ANOVA


One-way and two-way repeated measures ANOVA


Results have been tested against R and JASP.

Missing values are automatically removed (listwise deletion) using the pingouin.remove_rm_na() function. This could drastically decrease the power of the ANOVA if many missing values are present. In that case, it might be better to use linear mixed effects models.

If the between-subject groups are unbalanced (= unequal sample sizes), a type II ANOVA will be computed.


Compute a two-way mixed model ANOVA.

>>> from pingouin import mixed_anova, read_dataset
>>> df = read_dataset('mixed_anova')
>>> aov = mixed_anova(dv='Scores', between='Group',
...                   within='Time', subject='Subject', data=df)
>>> aov
        Source     SS  DF1  DF2     MS      F     p-unc    np2    eps
0        Group  5.460    1   58  5.460  5.052  0.028420  0.080      -
1         Time  7.628    2  116  3.814  4.027  0.020373  0.065  0.999
2  Interaction  5.168    2  116  2.584  2.728  0.069530  0.045      -