arviz.plot_hdi#

arviz.plot_hdi(x, y=None, hdi_prob=None, hdi_data=None, color='C1', circular=False, smooth=True, smooth_kwargs=None, figsize=None, fill_kwargs=None, plot_kwargs=None, hdi_kwargs=None, ax=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot HDI intervals for regression data.

Parameters

xarray_like: Values to plot.
yarray_like, optional: Values from which to compute the HDI. Assumed shape (chain, draw, \*shape). Only optional if hdi_data is present.
hdi_dataarray_like, optional: Precomputed HDI values to use. Assumed shape is (*x.shape, 2).
hdi_probfloat, optional: Probability for the highest density interval. Defaults to stats.hdi_prob rcParam. See this section for usage examples.
colorstr, default “C1”: Color used for the limits of the HDI and fill. Should be a valid matplotlib color.
circularbool, default False: Whether to compute the HDI taking into account x is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).
smoothbool, default True: If True the result will be smoothed by first computing a linear interpolation of the data over a regular grid and then applying the Savitzky-Golay filter to the interpolated data.
smooth_kwargsdict, optional: Additional keywords modifying the Savitzky-Golay filter. See scipy.signal.savgol_filter() for details.
figsize(float, float), optional: Figure size. If None, it will be defined automatically.
fill_kwargsdict, optional: Keywords passed to matplotlib.axes.Axes.fill_between() (use fill_kwargs={'alpha': 0} to disable fill) or to bokeh.plotting.Figure.patch().
plot_kwargsdict, optional: HDI limits keyword arguments, passed to matplotlib.axes.Axes.plot() or bokeh.plotting.Figure.patch().
hdi_kwargsdict, optional: Keyword arguments passed to hdi(). Ignored if hdi_data is present.
axaxes, optional: Matplotlib axes or bokeh figures.
backend{“matplotlib”, “bokeh”}, default “matplotlib”: Select plotting backend.
backend_kwargsdict, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure. For additional documentation check the plotting method of the backend.
showbool, optional: Call backend show function.

Returns

axesmatplotlib axes or bokeh figures

See also

hdi: Calculate highest density interval (HDI) of array for given probability.

Examples

Plot HDI interval of simulated random-walk data using y argument:

>>> import numpy as np
>>> import arviz as az
>>> # time-steps random walk
>>> x_data =np.arange(0,100)
>>> # Mean random walk
>>> mu = np.zeros(100)
>>> for i in x_data: mu[i] = mu[i-1] + np.random.normal(0, 1, 1)
>>> # Simulated pp samples form the random walk time series
>>> y_data = np.random.normal(2 + mu * 0.5, 0.5, size = (2, 50, 100))
>>> az.plot_hdi(x_data, y_data)

plot_hdi can also be given precalculated values with the argument hdi_data. This example shows how to use hdi() to precalculate the values and pass these values to plot_hdi. Similarly to an example in hdi we are using the input_core_dims argument of wrap_xarray_ufunc() to manually define the dimensions over which to calculate the HDI.

>>> hdi_data = az.hdi(y_data, input_core_dims=[["draw"]])
>>> ax = az.plot_hdi(x_data, hdi_data=hdi_data[0], color="r", fill_kwargs={"alpha": .2})
>>> az.plot_hdi(x_data, hdi_data=hdi_data[1], color="k", ax=ax, fill_kwargs={"alpha": .2})

plot_hdi can also be used with Inference Data objects. Here we use the posterior predictive to plot the HDI interval.

>>> X = np.random.normal(0,1,100)
>>> Y = np.random.normal(2 + X * 0.5, 0.5, size=(2,10,100))
>>> idata = az.from_dict(posterior={"y": Y}, constant_data={"x":X})
>>> x_data = idata.constant_data.x
>>> y_data = idata.posterior.y
>>> az.plot_hdi(x_data, y_data)