arviz_plots.plot_autocorr

arviz_plots.plot_autocorr(dt, var_names=None, filter_vars=None, group='posterior', coords=None, sample_dims=None, max_lag=None, plot_collection=None, backend=None, labeller=None, aes_by_visuals=None, visuals=None, **pc_kwargs)

Autocorrelation plots for the given dataset.

Line plot of the autocorrelation function (ACF)

The ACF plots can be used as a convergence diagnostic for posteriors from MCMC samples.

Parameters:

dtxarray.DataTree

Input data

var_namesstr or list of str, optional

One or more variables to be plotted. Currently only one variable is supported. 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.

groupstr, optional

Which group to use. Defaults to “posterior”.

coordsdict, optional

Coordinates to plot.

sample_dimsstr or sequence of hashable, optional

Dimensions to reduce unless mapped to an aesthetic. Defaults to rcParams["data.sample_dims"]

max_lagint, optional

Maximum lag to compute the ACF. Defaults to 100.

plot_collectionPlotCollection, optional

backend{“matplotlib”, “bokeh”, “plotly”}, optional

labellerlabeller, optional

aes_by_visualsmapping of {strsequence of str}, optional

Mapping of visuals to aesthetics that should use their mapping in plot_collection when plotted. Valid keys are the same as for visuals.

visualsmapping of {strmapping or bool}, optional

Valid keys are:

• lines -> passed to ecdf_line

• ref_line -> passed to line_xy

• credible_interval -> passed to fill_between_y

• xlabel -> passed to labelled_x

• title -> passed to labelled_title

**pc_kwargs

Passed to arviz_plots.PlotCollection.grid

Returns:

PlotCollection

Examples

Autocorrelation plot for mu variable in the centered eight dataset.

>>> from arviz_plots import plot_autocorr, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('centered_eight')
>>> plot_autocorr(dt, var_names=["mu"])

faceted plot with autocorrelation for each variable

Autocorrelation Plot

Autocorrelation Plot