pingouin.plot_rm_corr

pingouin.plot_rm_corr(data=None, x=None, y=None, subject=None, legend=False, kwargs_facetgrid={'aspect': 1, 'height': 4})[source]

Plot a repeated measures correlation.

Parameters

datapandas.DataFrame: Dataframe.
x, ystring: Name of columns in data containing the two dependent variables.
subjectstring: Name of column in data containing the subject indicator.
legendboolean: If True, add legend to plot. Legend will show all the unique values in subject.
kwargs_facetgriddict: Optional keyword argument passed to seaborn.FacetGrid

Returns

gseaborn.FacetGrid: Seaborn FacetGrid.

See also

rm_corr

Notes

Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals.

Results have been tested against the rmcorr R package. Note that this function requires the statsmodels Python package.

Missing values are automatically removed from the data (listwise deletion).

References

1: Bakdash, J.Z., Marusich, L.R., 2017. Repeated Measures Correlation. Front. Psychol. 8, 456. https://doi.org/10.3389/fpsyg.2017.00456
2: Bland, J. M., & Altman, D. G. (1995). Statistics notes: Calculating correlation coefficients with repeated observations: Part 1—correlation within subjects. Bmj, 310(6977), 446.
3: https://github.com/cran/rmcorr

Examples

Default repeated mesures correlation plot

>>> import pingouin as pg
>>> df = pg.read_dataset('rm_corr')
>>> g = pg.plot_rm_corr(data=df, x='pH', y='PacO2', subject='Subject')

With some tweakings

>>> import pingouin as pg
>>> import seaborn as sns
>>> df = pg.read_dataset('rm_corr')
>>> sns.set(style='darkgrid', font_scale=1.2)
>>> g = pg.plot_rm_corr(data=df, x='pH', y='PacO2',
...                     subject='Subject', legend=True,
...                     kwargs_facetgrid=dict(height=4.5, aspect=1.5,
...                                           palette='Spectral'))