arviz.plot_dist_comparison

arviz.plot_dist_comparison(data, kind='latent', figsize=None, textsize=None, var_names=None, coords=None, combine_dims=None, transform=None, legend=True, labeller=None, ax=None, prior_kwargs=None, posterior_kwargs=None, observed_kwargs=None, backend=None, backend_kwargs=None, show=None)

Plot to compare fitted and unfitted distributions.

The resulting plots will show the compared distributions both on separate axes (particularly useful when one of them is substantially tighter than another), and plotted together, displaying a grid of three plots per distribution.

Parameters:

dataInferenceData

Any object that can be converted to an arviz.InferenceData object containing the posterior/prior data. Refer to documentation of arviz.convert_to_dataset() for details.

kind{“latent”, “observed”}, default “latent”

kind of plot to display The “latent” option includes {“prior”, “posterior”}, and the “observed” option includes {“observed_data”, “prior_predictive”, “posterior_predictive”}.

figsize(float, float), optional

Figure size. If None it will be defined automatically.

textsizefloat

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.

var_namesstr, list, list of lists, optional

if str, plot the variable. if list, plot all the variables in list of all groups. if list of lists, plot the vars of groups in respective lists. See this section for usage examples.

coordsdict

Dictionary mapping dimensions to selected coordinates to be plotted. Dimensions without a mapping specified will include all coordinates for that dimension. See this section for usage examples.

combine_dimsset_like of str, optional

List of dimensions to reduce. Defaults to reducing only the “chain” and “draw” dimensions. See this section for usage examples.

transformcallable()

Function to transform data (defaults to None i.e. the identity function).

legendbool

Add legend to figure. By default True.

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.

ax(nvars, 3) array_like of matplotlib Axes, optional

Matplotlib axes: The ax argument should have shape (nvars, 3), where the last column is for the combined before/after plots and columns 0 and 1 are for the before and after plots, respectively.

prior_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for prior/predictive groups.

posterior_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for posterior/predictive groups.

observed_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for observed_data group.

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:

axes2D ndarray of matplotlib Axes

Returned object will have shape (nvars, 3), where the last column is the combined plot and the first columns are the single plots.

See also

plot_bpv

Plot Bayesian p-value for observed data and Posterior/Prior predictive.

Examples

Plot the prior/posterior plot for specified vars and coords.

>>> import arviz as az
>>> data = az.load_arviz_data('rugby')
>>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]})