`scipy.stats._fit:FitResult.plot`

source: /scipy/stats/_fit.py :113

Signature

def plot ( self , ax = None , * , plot_type = hist )

Summary

Visually compare the data against the fitted distribution.

Extended Summary

Available only if matplotlib is installed.

Parameters

ax : `matplotlib.axes.Axes`

Axes object to draw the plot onto, otherwise uses the current Axes.

plot_type : {"hist", "qq", "pp", "cdf"}

Type of plot to draw. Options include:

"hist": Superposes the PDF/PMF of the fitted distribution over a normalized histogram of the data.
"qq": Scatter plot of theoretical quantiles against the empirical quantiles. Specifically, the x-coordinates are the values of the fitted distribution PPF evaluated at the percentiles (np.arange(1, n) - 0.5)/n, where n is the number of data points, and the y-coordinates are the sorted data points.
"pp": Scatter plot of theoretical percentiles against the observed percentiles. Specifically, the x-coordinates are the percentiles (np.arange(1, n) - 0.5)/n, where n is the number of data points, and the y-coordinates are the values of the fitted distribution CDF evaluated at the sorted data points.
"cdf": Superposes the CDF of the fitted distribution over the empirical CDF. Specifically, the x-coordinates of the empirical CDF are the sorted data points, and the y-coordinates are the percentiles (np.arange(1, n) - 0.5)/n, where n is the number of data points.

Returns

ax : `matplotlib.axes.Axes`: The matplotlib Axes object on which the plot was drawn.

Examples

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt  # matplotlib must be installed
rng = np.random.default_rng()
data = stats.nbinom(5, 0.5).rvs(size=1000, random_state=rng)
bounds = [(0, 30), (0, 1)]
res = stats.fit(stats.nbinom, data, bounds)
ax = res.plot()  # save matplotlib Axes object

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The `matplotlib.axes.Axes` object can be used to customize the plot. See `matplotlib.axes.Axes` documentation for details.

ax.set_xlabel('number of trials')  # customize axis label

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ax.get_children()[0].set_linewidth(5)  # customize line widths

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ax.legend()

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plt.show()

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Aliases

scipy.stats._fit.FitResult.plot