arviz.plot_elpd#

arviz.plot_elpd(compare_dict, color='C0', xlabels=False, figsize=None, textsize=None, coords=None, legend=False, threshold=None, ax=None, ic=None, scale=None, var_name=None, plot_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot pointwise elpd differences between two or more models.

Parameters
compare_dictmapping of {strELPDData or InferenceData}

A dictionary mapping the model name to the object containing inference data or the result of arviz.loo() or arviz.waic() functions. Refer to arviz.convert_to_inference_data() for details on possible dict items.

colorstr or array_like, optional

Colors of the scatter plot. If color is a str all dots will have the same color. If it is the size of the observations, each dot will have the specified color. Otherwise, it will be interpreted as a list of the dims to be used for the color code.

xlabelsbool, optional

Use coords as xticklabels. Defaults to False.

figsizefigure size tuple, optional

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

textsize: int, optional

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

coordsmapping, optional

Coordinates of points to plot. All values are used for computation, but only a subset can be plotted for convenience.

legendbool, optional

Include a legend to the plot. Only taken into account when color argument is a dim name.

thresholdfloat

If some elpd difference is larger than threshold * elpd.std(), show its label. If None, no observations will be highlighted.

icstr, optional

Information Criterion (“loo” for PSIS-LOO, “waic” for WAIC) used to compare models. Defaults to rcParams["stats.information_criterion"]. Only taken into account when input is arviz.InferenceData.

scalestr, optional

Scale argument passed to arviz.loo() or arviz.waic(), see their docs for details. Only taken into account when values in compare_dict are arviz.InferenceData.

var_namestr, optional

Argument passed to to arviz.loo() or arviz.waic(), see their docs for details. Only taken into account when values in compare_dict are arviz.InferenceData.

plot_kwargsdicts, optional

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

ax: axes, optional

matplotlib.axes.Axes or bokeh.plotting.Figure.

backend: str, optional

Select plotting backend {“matplotlib”, “bokeh”}. Defaults to “matplotlib”.

backend_kwargs: bool, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure().

showbool, optional

Call backend show function.

Returns
axesmatplotlib axes or bokeh figures

See also

plot_compare

Summary plot for model comparison.

Examples

Compare pointwise PSIS-LOO for centered and non centered models of the 8-schools problem using matplotlib.

>>> import arviz as az
>>> idata1 = az.load_arviz_data("centered_eight")
>>> idata2 = az.load_arviz_data("non_centered_eight")
>>> az.plot_elpd(
>>>     {"centered model": idata1, "non centered model": idata2},
>>>     xlabels=True
>>> )
../../_images/arviz-plot_elpd-1.png 
import arviz as az
idata1 = az.load_arviz_data("centered_eight")
idata2 = az.load_arviz_data("non_centered_eight")
az.plot_elpd(
    {"centered model": idata1, "non centered model": idata2},
    backend="bokeh"
)

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