correlation_plot

normtest.looney_gulledge.correlation_plot(axes, x_data, weighted=False)

This function creates an axis with the Looney-Gulledge test [1] correlation graph.

Parameters:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

x_datanumpy array

One dimension numpy array with at least 4 observations.

weightedbool, optional

Whether to estimate the Normal order considering the repeats as its average (True) or not (False, default). Only has an effect if the dataset contains repeated values;

Returns:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

See also

test

dist_plot

References

[1]

LOONEY, S. W.; GULLEDGE, T. R. Use of the Correlation Coefficient with Normal Probability Plots. The American Statistician, v. 39, n. 1, p. 75-79, fev. 1985.

Examples

>>> from normtest import looney_gulledge
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> data = stats.norm.rvs(loc=0, scale=1, size=30, random_state=42)
>>> fig, ax = plt.subplots(figsize=(6, 4))
>>> looney_gulledge.correlation_plot(axes=ax, x_data=data)
>>> # plt.savefig("correlation_plot.png")
>>> plt.show()