arviz.kde¶

arviz.kde(x, circular=False, **kwargs)[source]¶

One dimensional density estimation.

It is a wrapper around kde_linear() and kde_circular().

Parameters

x1D numpy array: Data used to calculate the density estimation.
circular: bool, optional: Whether x is a circular variable or not. Defaults to False.
**kwargs: Arguments passed to `kde_linear()` and `kde_circular()`.: See their documentation for more info.

Returns

gridGridded numpy array for the x values.
pdfNumpy array for the density estimates.
bw: optional, the estimated bandwidth.

See also

plot_kde: Compute and plot a kernel density estimate.

Examples

Default density estimation for linear data .. plot:

:context: close-figs

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from arviz import kde
>>>
>>> rvs = np.random.gamma(shape=1.8, size=1000)
>>> grid, pdf = kde(rvs)
>>> plt.plot(grid, pdf)
>>> plt.show()

Density estimation for linear data with Silverman’s rule bandwidth .. plot:

:context: close-figs

>>> grid, pdf = kde(rvs, bw="silverman")
>>> plt.plot(grid, pdf)
>>> plt.show()

Density estimation for linear data with scaled bandwidth .. plot:

:context: close-figs

>>> # bw_fct > 1 means more smoothness.
>>> grid, pdf = kde(rvs, bw_fct=2.5)
>>> plt.plot(grid, pdf)
>>> plt.show()

Default density estimation for linear data with extended limits .. plot:

:context: close-figs

>>> grid, pdf = kde(rvs, bound_correction=False, extend=True, extend_fct=0.5)
>>> plt.plot(grid, pdf)
>>> plt.show()

Default density estimation for linear data with custom limits .. plot:

:context: close-figs

# It accepts tuples and lists of length 2.

>>> grid, pdf = kde(rvs, bound_correction=False, custom_lims=(0, 10))
>>> plt.plot(grid, pdf)
>>> plt.show()

Default density estimation for circular data .. plot:

:context: close-figs

>>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500)
>>> grid, pdf = kde(rvs, circular=True)
>>> plt.plot(grid, pdf)
>>> plt.show()

Density estimation for circular data with scaled bandwidth .. plot:

:context: close-figs

>>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500)
>>> # bw_fct > 1 means less smoothness.
>>> grid, pdf = kde(rvs, circular=True, bw_fct=3)
>>> plt.plot(grid, pdf)
>>> plt.show()

Density estimation for circular data with custom limits .. plot:

:context: close-figs

>>> # This is still experimental, does not always work.
>>> rvs = np.random.vonmises(mu=0, kappa=30, size=500)
>>> grid, pdf = kde(rvs, circular=True, custom_lims=(-1, 1))
>>> plt.plot(grid, pdf)
>>> plt.show()

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