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.

Pointwise model comparison based on their expected log pointwise predictive density (ELPD).

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, default “C0”

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, default False

Use coords as xticklabels.

figsize(float, float), optional

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

textsizefloat, 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. See this section for usage examples.

legendbool, default False

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

thresholdfloat, optional

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().

axaxes, optional

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

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 Figure

See also

plot_compare

Summary plot for model comparison.

loo

Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV).

waic

Compute the widely applicable information criterion.

Notes

The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out cross-validation (LOO) or using the widely applicable information criterion (WAIC). We recommend LOO in line with the work presented by [1].

References

[1]

Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC https://arxiv.org/abs/1507.04544

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"
)