Loading [MathJax]/extensions/tex2jax.js
scipy 1.10.1 Pypi GitHub Homepage
Other Docs

ParametersReturnsBackRef
ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80)

The probability plot correlation coefficient (PPCC) plot can be used to determine the optimal shape parameter for a one-parameter family of distributions. It cannot be used for distributions without shape parameters (like the normal distribution) or with multiple shape parameters.

By default a Tukey-Lambda distribution (stats.tukeylambda) is used. A Tukey-Lambda PPCC plot interpolates from long-tailed to short-tailed distributions via an approximately normal one, and is therefore particularly useful in practice.

Parameters

x : array_like

Input array.

a, b : scalar

Lower and upper bounds of the shape parameter to use.

dist : str or stats.distributions instance, optional

Distribution or distribution function name. Objects that look enough like a stats.distributions instance (i.e. they have a ppf method) are also accepted. The default is 'tukeylambda'.

plot : object, optional

If given, plots PPCC against the shape parameter. plot is an object that has to have methods "plot" and "text". The matplotlib.pyplot(?) module or a Matplotlib Axes object can be used, or a custom object with the same methods. Default is None, which means that no plot is created.

N : int, optional

Number of points on the horizontal axis (equally distributed from a to b).

Returns

svals : ndarray

The shape values for which ppcc was calculated.

ppcc : ndarray

The calculated probability plot correlation coefficient values.

Calculate and optionally plot probability plot correlation coefficient.

See Also

boxcox_normplot
ppcc_max
probplot
tukeylambda

Examples

First we generate some random data from a Weibull distribution with shape parameter 2.5, and plot the histogram of the data:
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
rng = np.random.default_rng()
c = 2.5
x = stats.weibull_min.rvs(c, scale=4, size=2000, random_state=rng)
Take a look at the histogram of the data.
fig1, ax = plt.subplots(figsize=(9, 4))
ax.hist(x, bins=50)
ax.set_title('Histogram of x')
plt.show()
Now we explore this data with a PPCC plot as well as the related probability plot and Box-Cox normplot. A red line is drawn where we expect the PPCC value to be maximal (at the shape parameter ``c`` used above):
fig2 = plt.figure(figsize=(12, 4))
ax1 = fig2.add_subplot(1, 3, 1)
ax2 = fig2.add_subplot(1, 3, 2)
ax3 = fig2.add_subplot(1, 3, 3)
res = stats.probplot(x, plot=ax1)
res = stats.boxcox_normplot(x, -4, 4, plot=ax2)
res = stats.ppcc_plot(x, c/2, 2*c, dist='weibull_min', plot=ax3)
ax3.axvline(c, color='r')
plt.show()
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.stats._morestats:ppcc_max

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

scipy.stats._morestats:boxcox_normplot_morestats:boxcox_normplotscipy.stats._morestats:ppcc_max_morestats:ppcc_maxtukeylambdatukeylambdamatplotlib.pyplotpyplotscipy.stats._morestats:probplot_morestats:probplot

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/stats/_morestats.py#736
type: <class 'function'>
Commit: