arviz.plot_mcse

arviz.plot_mcse(idata, var_names=None, filter_vars=None, coords=None, errorbar=False, grid=None, figsize=None, textsize=None, extra_methods=False, rug=False, rug_kind='diverging', n_points=20, labeller=None, ax=None, rug_kwargs=None, extra_kwargs=None, text_kwargs=None, backend=None, backend_kwargs=None, show=None, **kwargs)

Plot quantile or local Monte Carlo Standard Error.

Parameters

idata: obj

Any object that can be converted to an arviz.InferenceData object Refer to documentation of arviz.convert_to_dataset() for details

var_names: list of variable names, optional

Variables to be plotted. Prefix the variables by ~ when you want to exclude them from the plot.

filter_vars: {None, “like”, “regex”}, optional, default=None

If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A la pandas.filter.

coords: dict, optional

Coordinates of var_names to be plotted. Passed to xarray.Dataset.sel()

errorbar: bool, optional

Plot quantile value +/- mcse instead of plotting mcse.

gridtuple

Number of rows and columns. Defaults to None, the rows and columns are automatically inferred.

figsize: tuple, optional

Figure size. If None it will be defined automatically.

textsize: float, optional

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

extra_methods: bool, optional

Plot mean and sd MCSE as horizontal lines. Only taken into account when errorbar=False.

rug: bool

Plot rug plot of values diverging or that reached the max tree depth.

rug_kind: bool

Variable in sample stats to use as rug mask. Must be a boolean variable.

n_points: int

Number of points for which to plot their quantile/local ess or number of subsets in the evolution plot.

labellerlabeller instance, optional

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

ax: numpy array-like of matplotlib axes or bokeh figures, optional

A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it).

rug_kwargs: dict

kwargs passed to rug plot in matplotlib.axes.Axes.plot() or bokeh.models.glyphs.Scatter.

extra_kwargs: dict, optional

kwargs passed as extra method lines in matplotlib.axes.Axes.axhline() or bokeh.models.Span

text_kwargs: dict, optional

kwargs passed to matplotlib.axes.Axes.annotate() for extra methods lines labels. It accepts the additional key x to set xy=(text_kwargs["x"], mcse). text_kwargs are ignored for the bokeh plotting backend.

backend: str, optional

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

backend_kwargs: bool, optional

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

show: bool, optional

Call backend show function.

**kwargs

Passed as-is to matplotlib.axes.Axes.hist() or matplotlib.axes.Axes.plot() in matplotlib depending on the value of kind.

Returns

axes: matplotlib axes or bokeh figures

See also

arviz.mcse()

Calculate Markov Chain Standard Error statistic.

References

• Vehtari et al. (2019) see https://arxiv.org/abs/1903.08008

Examples

Plot quantile Monte Carlo Standard Error.

>>> import arviz as az
>>> idata = az.load_arviz_data("centered_eight")
>>> coords = {"school": ["Deerfield", "Lawrenceville"]}
>>> az.plot_mcse(
...     idata, var_names=["mu", "theta"], coords=coords
... )

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