arviz.plot_autocorr#

arviz.plot_autocorr(data, var_names=None, filter_vars=None, max_lag=None, combined=False, grid=None, figsize=None, textsize=None, labeller=None, ax=None, backend=None, backend_config=None, backend_kwargs=None, show=None)[source]#

Bar plot of the autocorrelation function for a sequence of data.

Useful in particular for posteriors from MCMC samples which may display correlation.

Parameters

data: 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, if None all variables are plotted. Prefix the variables by ~ when you want to exclude them from the plot. Vector-value stochastics are handled automatically.
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.
max_lag: int, optional: Maximum lag to calculate autocorrelation. Defaults to 100 or num draws, whichever is smaller.
combined: bool, default=False: Flag for combining multiple chains into a single chain. If False, chains will be plotted separately.
gridtuple: Number of rows and columns. Defaults to None, the rows and columns are automatically inferred.
figsize: tuple: Figure size. If None it will be defined automatically. Note this is not used if ax is supplied.
textsize: float: Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.
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).
backend: str, optional: Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.
backend_config: dict, optional: Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams.
backend_kwargs: dict, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure().
show: bool, optional: Call backend show function.

Returns

axes: matplotlib axes or bokeh figures

See also

autocov: Compute autocovariance estimates for every lag for the input array.
autocorr: Compute autocorrelation using FFT for every lag for the input array.

Examples

Plot default autocorrelation

>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_autocorr(data)

Plot subset variables by specifying variable name exactly

>>> az.plot_autocorr(data, var_names=['mu', 'tau'] )

Combine chains by variable and select variables by excluding some with partial naming

>>> az.plot_autocorr(data, var_names=['~thet'], filter_vars="like", combined=True)

Specify maximum lag (x axis bound)

>>> az.plot_autocorr(data, var_names=['mu', 'tau'], max_lag=200, combined=True)

Plots

arviz.plot_bpv