pingouin.plot_shift#

pingouin.plot_shift(x, y, paired=False, n_boot=1000, percentiles=array([10, 20, 30, 40, 50, 60, 70, 80, 90]), confidence=0.95, seed=None, show_median=True, violin=True)[source]#

Shift plot.

Parameters:

x, yarray_like: First and second set of observations.
pairedbool: Specify whether x and y are related (i.e. repeated measures) or independent.

Added in version 0.3.0.
n_bootint: Number of bootstrap iterations. The higher, the better, the slower.
percentiles: array_like: Sequence of percentiles to compute, which must be between 0 and 100 inclusive. Default set to [10, 20, 30, 40, 50, 60, 70, 80, 90].
confidencefloat: Confidence level (0.95 = 95%) for the confidence intervals.
seedint or None: Random seed for generating bootstrap samples, can be integer or None for no seed (default).
show_median: boolean: If True (default), show the median with black lines.
violin: boolean: If True (default), plot the density of X and Y distributions. Defaut set to True.

Returns:

figmatplotlib Figure instance: Matplotlib Figure. To get the individual axes, use fig.axes.

See also

harrelldavis

Notes

The shift plot is described in [1]. It computes a shift function [2] for two (in)dependent groups using the robust Harrell-Davis quantile estimator in conjunction with bias-corrected bootstrap confidence intervals.

References

[1]

Rousselet, G. A., Pernet, C. R. and Wilcox, R. R. (2017). Beyond differences in means: robust graphical methods to compare two groups in neuroscience. Eur J Neurosci, 46: 1738-1748. doi:10.1111/ejn.13610

[2]

https://garstats.wordpress.com/2016/07/12/shift-function/

Examples

Default shift plot

>>> import numpy as np
>>> import pingouin as pg
>>> np.random.seed(42)
>>> x = np.random.normal(5.5, 2, 50)
>>> y = np.random.normal(6, 1.5, 50)
>>> fig = pg.plot_shift(x, y)

With different options, and custom axes labels

>>> import pingouin as pg
>>> import matplotlib.pyplot as plt
>>> data = pg.read_dataset("pairwise_corr")
>>> fig = pg.plot_shift(data["Neuroticism"], data["Conscientiousness"], paired=True,
...                     n_boot=2000, percentiles=[25, 50, 75], show_median=False, seed=456,
...                     violin=False)
>>> fig.axes[0].set_xlabel("Groups")
>>> fig.axes[0].set_ylabel("Values", size=15)
>>> fig.axes[0].set_title("Comparing Neuroticism and Conscientiousness", size=15)
>>> fig.axes[1].set_xlabel("Neuroticism quantiles", size=12)
>>> plt.tight_layout()