arviz.plot_loo_pit — ArviZ dev documentation (original) (raw)

arviz.plot_loo_pit(idata=None, y=None, y_hat=None, log_weights=None, ecdf=False, ecdf_fill=True, n_unif=100, use_hdi=False, hdi_prob=None, figsize=None, textsize=None, labeller=None, color='C0', legend=True, ax=None, plot_kwargs=None, plot_unif_kwargs=None, hdi_kwargs=None, fill_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot Leave-One-Out (LOO) probability integral transformation (PIT) predictive checks.

Parameters:

idataInferenceData

arviz.InferenceData object.

yarray, DataArray or str

Observed data. If str, idata must be present and contain the observed data group

y_hatarray, DataArray or str

Posterior predictive samples for y. It must have the same shape as y plus an extra dimension at the end of size n_samples (chains and draws stacked). If str or None, idata must contain the posterior predictive group. If None, y_hat is taken equal to y, thus, y must be str too.

log_weightsarray or DataArray

Smoothed log_weights. It must have the same shape as y_hat

ecdfbool, optional

Plot the difference between the LOO-PIT Empirical Cumulative Distribution Function (ECDF) and the uniform CDF instead of LOO-PIT kde. In this case, instead of overlaying uniform distributions, the beta hdi_probaround the theoretical uniform CDF is shown. This approximation only holds for large S and ECDF values not very close to 0 nor 1. For more information, seeVehtari et al. (2021), Appendix G.

ecdf_fillbool, optional

Use matplotlib.axes.Axes.fill_between() to mark the area inside the credible interval. Otherwise, plot the border lines.

n_unifint, optional

Number of datasets to simulate and overlay from the uniform distribution.

use_hdibool, optional

Compute expected hdi values instead of overlaying the sampled uniform distributions.

hdi_probfloat, optional

Probability for the highest density interval. Works with use_hdi=True or ecdf=True.

figsize(float, float), optional

If None, size is (8 + numvars, 8 + numvars)

textsizeint, optional

Text size for labels. If None it will be autoscaled based on figsize.

labellerLabeller, optional

Class providing the method make_pp_label to generate the labels in the plot titles. Read the Label guide for more details and usage examples.

colorstr or array_like, optional

Color of the LOO-PIT estimated pdf plot. If plot_unif_kwargs has no “color” key, a slightly lighter color than this argument will be used for the uniform kde lines. This will ensure that LOO-PIT kde and uniform kde have different default colors.

legendbool, optional

Show the legend of the figure.

axaxes, optional

Matplotlib axes or bokeh figures.

plot_kwargsdict, optional

Additional keywords passed to matplotlib.axes.Axes.plot()for LOO-PIT line (kde or ECDF)

plot_unif_kwargsdict, optional

Additional keywords passed to matplotlib.axes.Axes.plot() for overlaid uniform distributions or for beta credible interval lines if ecdf=True

hdi_kwargsdict, optional

Additional keywords passed to matplotlib.axes.Axes.axhspan()

fill_kwargsdict, optional

Additional kwargs passed to matplotlib.axes.Axes.fill_between()

backendstr, optional

Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.

backend_kwargsbool, optional

These are kwargs specific to the backend being used, passed tomatplotlib.pyplot.subplots() orbokeh.plotting.figure(). For additional documentation check the plotting method of the backend.

showbool, optional

Call backend show function.

Returns:

axesmatplotlib Axes or bokeh_figures