Plotting functions
ArviZPythonPlots.plot_autocorrArviZPythonPlots.plot_bfArviZPythonPlots.plot_bpvArviZPythonPlots.plot_compareArviZPythonPlots.plot_densityArviZPythonPlots.plot_distArviZPythonPlots.plot_dist_comparisonArviZPythonPlots.plot_dotArviZPythonPlots.plot_ecdfArviZPythonPlots.plot_elpdArviZPythonPlots.plot_energyArviZPythonPlots.plot_essArviZPythonPlots.plot_forestArviZPythonPlots.plot_hdiArviZPythonPlots.plot_kdeArviZPythonPlots.plot_khatArviZPythonPlots.plot_lmArviZPythonPlots.plot_loo_pitArviZPythonPlots.plot_mcseArviZPythonPlots.plot_pairArviZPythonPlots.plot_parallelArviZPythonPlots.plot_posteriorArviZPythonPlots.plot_ppcArviZPythonPlots.plot_rankArviZPythonPlots.plot_separationArviZPythonPlots.plot_traceArviZPythonPlots.plot_violin
Reference
ArviZPythonPlots.plot_autocorr — Method
Bar plot of the autocorrelation function (ACF) for a sequence of data.
The ACF plots are helpful as a convergence diagnostic for posteriors from MCMC
samples which display autocorrelation.
Parameters
----------
data : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names : list of str, optional
Variables to be plotted. Prefix the variables by ``~`` when you want to exclude
them from the plot. See :ref:`this section <common_var_names>` for usage examples.
filter_vars : {None, "like", "regex"}, 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. See
:ref:`this section <common_filter_vars>` for usage examples.
coords: mapping, optional
Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`
max_lag : int, optional
Maximum lag to calculate autocorrelation. By Default, the plot displays the
first 100 lag or the total number of draws, whichever is smaller.
combined : bool, default False
Flag for combining multiple chains into a single chain. If False, chains will be
plotted separately.
grid : tuple, optional
Number of rows and columns. Defaults to None, the rows and columns are
automatically inferred. See :ref:`this section <common_grid>` for usage examples.
figsize : (float, float), optional
Figure size. If None it will be defined automatically.
Note this is not used if `ax` is supplied.
textsize : float, optional
Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
on `figsize`.
labeller : Labeller, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ax : 2D array-like of matplotlib_axes or bokeh_figure, 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 : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
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
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
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
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_autocorr(data)
Plot subset variables by specifying variable name exactly
.. plot::
:context: close-figs
>>> az.plot_autocorr(data, var_names=['mu', 'tau'] )
Combine chains by variable and select variables by excluding some with partial naming
.. plot::
:context: close-figs
>>> az.plot_autocorr(data, var_names=['~thet'], filter_vars="like", combined=True)
Specify maximum lag (x axis bound)
.. plot::
:context: close-figs
>>> az.plot_autocorr(data, var_names=['mu', 'tau'], max_lag=200, combined=True)
ArviZPythonPlots.plot_bf — Method
Approximated Bayes Factor for comparing hypothesis of two nested models.
The Bayes factor is estimated by comparing a model (H1) against a model in which the
parameter of interest has been restricted to be a point-null (H0). This computation
assumes the models are nested and thus H0 is a special case of H1.
Notes
-----
The bayes Factor is approximated as the Savage-Dickey density ratio
algorithm presented in [1]_.
Parameters
----------
idata : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details.
var_name : str, optional
Name of variable we want to test.
prior : numpy.array, optional
In case we want to use different prior, for example for sensitivity analysis.
ref_val : int, default 0
Point-null for Bayes factor estimation.
colors : tuple, default ('C0', 'C1')
Tuple of valid Matplotlib colors. First element for the prior, second for the posterior.
figsize : (float, float), 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 auto
scaled based on `figsize`.
plot_kwargs : dict, optional
Additional keywords passed to :func:`matplotlib.pyplot.plot`.
hist_kwargs : dict, optional
Additional keywords passed to :func:`arviz.plot_dist`. Only works for discrete variables.
ax : axes, optional
:class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
dict : A dictionary with BF10 (Bayes Factor 10 (H1/H0 ratio), and BF01 (H0/H1 ratio).
axes : matplotlib_axes or bokeh_figure
References
----------
.. [1] Heck, D., 2019. A caveat on the Savage-Dickey density ratio:
The case of computing Bayes factors for regression parameters.
Examples
--------
Moderate evidence indicating that the parameter "a" is different from zero.
.. plot::
:context: close-figs
>>> import numpy as np
>>> import arviz as az
>>> idata = az.from_dict(posterior={"a":np.random.normal(1, 0.5, 5000)},
... prior={"a":np.random.normal(0, 1, 5000)})
>>> az.plot_bf(idata, var_name="a", ref_val=0)
ArviZPythonPlots.plot_bpv — Method
Plot Bayesian p-value for observed data and Posterior/Prior predictive.
Parameters
----------
data : InferenceData
:class:`arviz.InferenceData` object containing the observed and
posterior/prior predictive data.
kind : {"u_value", "p_value", "t_stat"}, default "u_value"
Specify the kind of plot:
* The ``kind="p_value"`` computes :math:`p := p(y* \leq y | y)`.
This is the probability of the data y being larger or equal than the predicted data y*.
The ideal value is 0.5 (half the predictions below and half above the data).
* The ``kind="u_value"`` argument computes :math:`p_i := p(y_i* \leq y_i | y)`.
i.e. like a p_value but per observation :math:`y_i`. This is also known as marginal
p_value. The ideal distribution is uniform. This is similar to the LOO-PIT
calculation/plot, the difference is than in LOO-pit plot we compute
:math:`pi = p(y_i* r \leq y_i | y_{-i} )`, where :math:`y_{-i}`,
is all other data except :math:`y_i`.
* The ``kind="t_stat"`` argument computes :math:`:= p(T(y)* \leq T(y) | y)`
where T is any test statistic. See ``t_stat`` argument below for details
of available options.
t_stat : str, float, or callable, default "median"
Test statistics to compute from the observations and predictive distributions.
Allowed strings are “mean”, “median” or “std”. Alternative a quantile can be passed
as a float (or str) in the interval (0, 1). Finally a user defined function is also
acepted, see examples section for details.
bpv : bool, default True
If True add the Bayesian p_value to the legend when ``kind = t_stat``.
plot_mean : bool, default True
Whether or not to plot the mean test statistic.
reference : {"analytical", "samples", None}, default "analytical"
How to compute the distributions used as reference for ``kind=u_values``
or ``kind=p_values``. Use `None` to not plot any reference.
smoothing : bool, optional
If True and the data has integer dtype, smooth the data before computing the p-values,
u-values or tstat. By default, True when `kind` is "u_value" and False otherwise.
mse : bool, default False
Show scaled mean square error between uniform distribution and marginal p_value
distribution.
n_ref : int, default 100
Number of reference distributions to sample when ``reference=samples``.
hdi_prob : float, optional
Probability for the highest density interval for the analytical reference distribution when
``kind=u_values``. Should be in the interval (0, 1]. Defaults to the
rcParam ``stats.ci_prob``. See :ref:`this section <common_hdi_prob>` for usage examples.
color : str, optional
Matplotlib color
grid : tuple, optional
Number of rows and columns. By default, the rows and columns are
automatically inferred. See :ref:`this section <common_grid>` for usage examples.
figsize : (float, float), 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`.
data_pairs : dict, optional
Dictionary containing relations between observed data and posterior/prior predictive data.
Dictionary structure:
- key = data var_name
- value = posterior/prior predictive var_name
For example, ``data_pairs = {'y' : 'y_hat'}``
If None, it will assume that the observed data and the posterior/prior
predictive data have the same variable name.
Labeller : Labeller, optional
Class providing the method ``make_pp_label`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
var_names : list of str, optional
Variables to be plotted. If `None` all variable are plotted. Prefix the variables by ``~``
when you want to exclude them from the plot. See the :ref:`this section <common_var_names>`
for usage examples. See :ref:`this section <common_var_names>` for usage examples.
filter_vars : {None, "like", "regex"}, 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. See
:ref:`this section <common_filter_vars>` for usage examples.
coords : dict, optional
Dictionary mapping dimensions to selected coordinates to be plotted.
Dimensions without a mapping specified will include all coordinates for
that dimension. Defaults to including all coordinates for all
dimensions if None. See :ref:`this section <common_coords>` for usage examples.
flatten : list, optional
List of dimensions to flatten in observed_data. Only flattens across the coordinates
specified in the coords argument. Defaults to flattening all of the dimensions.
flatten_pp : list, optional
List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens
across the coordinates specified in the coords argument. Defaults to flattening all
of the dimensions. Dimensions should match flatten excluding dimensions for data_pairs
parameters. If `flatten` is defined and `flatten_pp` is None, then ``flatten_pp=flatten``.
legend : bool, default True
Add legend to figure.
ax : 2D array-like of matplotlib_axes or bokeh_figure, 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".
plot_ref_kwargs : dict, optional
Extra keyword arguments to control how reference is represented.
Passed to :meth:`matplotlib.axes.Axes.plot` or
:meth:`matplotlib.axes.Axes.axhspan` (when ``kind=u_value``
and ``reference=analytical``).
backend_kwargs : bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
group : {"posterior", "prior"}, default "posterior"
Specifies which InferenceData group should be plotted. If "posterior", then the values
in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then
the same comparison happens, but with the values in `prior_predictive` group.
show : bool, optional
Call backend show function.
Returns
-------
axes : 2D ndarray of matplotlib_axes or bokeh_figure
See Also
--------
plot_ppc : Plot for posterior/prior predictive checks.
plot_loo_pit : Plot Leave-One-Out probability integral transformation (PIT) predictive checks.
plot_dist_comparison : Plot to compare fitted and unfitted distributions.
References
----------
* Gelman et al. (2013) see http://www.stat.columbia.edu/~gelman/book/ pages 151-153 for details
Notes
-----
Discrete data is smoothed before computing either p-values or u-values using the
function :func:`~arviz.smooth_data` if the data is integer type
and the smoothing parameter is True.
Examples
--------
Plot Bayesian p_values.
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data("regression1d")
>>> az.plot_bpv(data, kind="p_value")
Plot custom test statistic comparison.
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data("regression1d")
>>> az.plot_bpv(data, kind="t_stat", t_stat=lambda x:np.percentile(x, q=50, axis=-1))
ArviZPythonPlots.plot_compare — Method
Summary plot for model comparison.
Models are compared based on their expected log pointwise predictive density (ELPD).
This plot is in the style of the one used in [2]_. Chapter 6 in the first edition
or 7 in the second.
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]_.
Parameters
----------
comp_df : pandas.DataFrame
Result of the :func:`arviz.compare` method.
insample_dev : bool, default False
Plot in-sample ELPD, that is the value of the information criteria without the
penalization given by the effective number of parameters (p_loo or p_waic).
plot_standard_error : bool, default True
Plot the standard error of the ELPD.
plot_ic_diff : bool, default False
Plot standard error of the difference in ELPD between each model
and the top-ranked model.
order_by_rank : bool, default True
If True ensure the best model is used as reference.
legend : bool, default False
Add legend to figure.
figsize : (float, float), optional
If `None`, size is (6, num of models) inches.
title : bool, default True
Show a tittle with a description of how to interpret the plot.
textsize : float, optional
Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based
on `figsize`.
labeller : Labeller, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
plot_kwargs : dict, optional
Optional arguments for plot elements. Currently accepts 'color_ic',
'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse',
'marker_dse', 'ls_min_ic' 'color_ls_min_ic', 'fontsize'
ax : matplotlib_axes or bokeh_figure, optional
Matplotlib axes or bokeh figure.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib_axes or bokeh_figure
See Also
--------
plot_elpd : Plot pointwise elpd differences between two or more models.
compare : Compare models based on PSIS-LOO loo or WAIC waic cross-validation.
loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV).
waic : Compute the widely applicable information criterion.
References
----------
.. [1] Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out
cross-validation and WAIC https://arxiv.org/abs/1507.04544
.. [2] McElreath R. (2022). Statistical Rethinking A Bayesian Course with Examples in
R and Stan, Second edition, CRC Press.
Examples
--------
Show default compare plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> model_compare = az.compare({'Centered 8 schools': az.load_arviz_data('centered_eight'),
>>> 'Non-centered 8 schools': az.load_arviz_data('non_centered_eight')})
>>> az.plot_compare(model_compare)
Include the in-sample ELDP
.. plot::
:context: close-figs
>>> az.plot_compare(model_compare, insample_dev=True)
ArviZPythonPlots.plot_density — Method
Generate KDE plots for continuous variables and histograms for discrete ones.
Plots are truncated at their 100*(1-alpha)% highest density intervals. Plots are grouped per
variable and colors assigned to models.
Parameters
----------
data : InferenceData or iterable of InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object, or an Iterator
returning a sequence of such objects.
Refer to documentation of :func:`arviz.convert_to_dataset` for details.
group : {"posterior", "prior"}, default "posterior"
Specifies which InferenceData group should be plotted. If "posterior", then the values
in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then
the same comparison happens, but with the values in `prior_predictive` group.
data_labels : list of str, default None
List with names for the datasets passed as "data." Useful when plotting more than one
dataset. Must be the same shape as the data parameter.
var_names : list of str, optional
List of variables to plot. If multiple datasets are supplied and `var_names` is not None,
will print the same set of variables for each dataset. Defaults to None, which results in
all the variables being plotted.
filter_vars : {None, "like", "regex"}, 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. See
:ref:`this section <common_filter_vars>` for usage examples.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See :ref:`this section <common_combine_dims>` for usage examples.
transform : callable
Function to transform data (defaults to `None` i.e. the identity function).
hdi_prob : float, default 0.94
Probability for the highest density interval. Should be in the interval (0, 1].
See :ref:`this section <common_hdi_prob>` for usage examples.
point_estimate : str, optional
Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None.
Defaults to 'auto' i.e. it falls back to default set in ``rcParams``.
colors : str or list of str, optional
List with valid matplotlib colors, one color per model. Alternative a string can be passed.
If the string is `cycle`, it will automatically choose a color per model from matplotlib's
cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all
models. Defaults to `cycle`.
outline : bool, default True
Use a line to draw KDEs and histograms.
hdi_markers : str
A valid `matplotlib.markers` like 'v', used to indicate the limits of the highest density
interval. Defaults to empty string (no marker).
shade : float, default 0
Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1
(opaque).
bw : float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental" when `circular` is False
and "taylor" (for now) when `circular` is True.
Defaults to "default" which means "experimental" when variable is not circular
and "taylor" when it is.
circular : bool, default False
If True, it interprets the values passed are from a circular variable measured in radians
and a circular KDE is used. Only valid for 1D KDE.
grid : tuple, optional
Number of rows and columns. Defaults to ``None``, the rows and columns are
automatically inferred. See :ref:`this section <common_grid>` for usage examples.
figsize : (float, float), 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`.
labeller : Labeller, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ax : 2D array-like of matplotlib_axes or bokeh_figure, 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 : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : 2D ndarray of matplotlib_axes or bokeh_figure
See Also
--------
plot_dist : Plot distribution as histogram or kernel density estimates.
plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book.
Examples
--------
Plot default density plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> centered = az.load_arviz_data('centered_eight')
>>> non_centered = az.load_arviz_data('non_centered_eight')
>>> az.plot_density([centered, non_centered])
Plot variables in a 4x5 grid
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], grid=(4, 5))
Plot subset variables by specifying variable name exactly
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], var_names=["mu"])
Plot a specific `az.InferenceData` group
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], var_names=["mu"], group="prior")
Specify highest density interval
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], var_names=["mu"], hdi_prob=.5)
Shade plots and/or remove outlines
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], var_names=["mu"], outline=False, shade=.8)
Specify binwidth for kernel density estimation
.. plot::
:context: close-figs
>>> az.plot_density([centered, non_centered], var_names=["mu"], bw=.9)
ArviZPythonPlots.plot_dist — Method
Plot distribution as histogram or kernel density estimates.
By default continuous variables are plotted using KDEs and discrete ones using histograms
Parameters
----------
values : array-like
Values to plot from an unknown continuous or discrete distribution.
values2 : array-like, optional
Values to plot. If present, a 2D KDE or a hexbin will be estimated.
color : string
valid matplotlib color.
kind : string, default "auto"
By default ("auto") continuous variables will use the kind defined by rcParam
``plot.density_kind`` and discrete ones will use histograms.
To override this use "hist" to plot histograms and "kde" for KDEs.
cumulative : bool, default False
If true plot the estimated cumulative distribution function. Defaults to False.
Ignored for 2D KDE.
label : string
Text to include as part of the legend.
rotated : bool, default False
Whether to rotate the 1D KDE plot 90 degrees.
rug : bool, default False
Add a `rug plot <https://en.wikipedia.org/wiki/Rug_plot>`_ for a specific subset
of values. Ignored for 2D KDE.
bw : float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental" when ``is_circular`` is False
and "taylor" (for now) when ``is_circular`` is True.
Defaults to "experimental" when variable is not circular and "taylor" when it is.
quantiles : list, optional
Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles.
contour : bool, default True
If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE.
fill_last : bool, default True
If True fill the last contour of the 2D KDE plot.
figsize : (float, float), 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`. Not implemented for bokeh backend.
plot_kwargs : dict
Keywords passed to the pdf line of a 1D KDE. Passed to :func:`arviz.plot_kde` as
``plot_kwargs``.
fill_kwargs : dict
Keywords passed to the fill under the line (use fill_kwargs={'alpha': 0} to disable fill).
Ignored for 2D KDE. Passed to :func:`arviz.plot_kde` as ``fill_kwargs``.
rug_kwargs : dict
Keywords passed to the rug plot. Ignored if ``rug=False`` or for 2D KDE
Use ``space`` keyword (float) to control the position of the rugplot.
The larger this number the lower the rugplot. Passed to
:func:`arviz.plot_kde` as ``rug_kwargs``.
contour_kwargs : dict
Keywords passed to the contourplot. Ignored for 1D KDE.
contourf_kwargs : dict
Keywords passed to :meth:`matplotlib.axes.Axes.contourf`. Ignored for 1D KDE.
pcolormesh_kwargs : dict
Keywords passed to :meth:`matplotlib.axes.Axes.pcolormesh`. Ignored for 1D KDE.
hist_kwargs : dict
Keyword arguments used to customize the histogram. Ignored when plotting a KDE.
They are passed to :meth:`matplotlib.axes.Axes.hist` if using matplotlib,
or to :meth:`bokeh.plotting.figure.quad` if using bokeh. In bokeh case,
the following extra keywords are also supported:
* ``color``: replaces the ``fill_color`` and ``line_color`` of the ``quad`` method
* ``bins``: taken from ``hist_kwargs`` and passed to :func:`numpy.histogram` instead
* ``density``: normalize histogram to represent a probability density function,
Defaults to ``True``
* ``cumulative``: plot the cumulative counts. Defaults to ``False``.
is_circular : {False, True, "radians", "degrees"}, default False
Select input type {"radians", "degrees"} for circular histogram or KDE plot. If True,
default input type is "radians". When this argument is present, it interprets the
values passed are from a circular variable measured in radians and a circular KDE is
used. Inputs in "degrees" will undergo an internal conversion to radians. Only valid
for 1D KDE.
ax : matplotlib_axes or bokeh_figure, optional
Matplotlib or bokeh targets on which to plot. If not supplied, Arviz will create
its own plot area (and return it).
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs :dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib axes or bokeh figure
See Also
--------
plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book.
plot_density : Generate KDE plots for continuous variables and histograms for discrete ones.
plot_kde : 1D or 2D KDE plot taking into account boundary conditions.
Examples
--------
Plot an integer distribution
.. plot::
:context: close-figs
>>> import numpy as np
>>> import arviz as az
>>> a = np.random.poisson(4, 1000)
>>> az.plot_dist(a)
Plot a continuous distribution
.. plot::
:context: close-figs
>>> b = np.random.normal(0, 1, 1000)
>>> az.plot_dist(b)
Add a rug under the Gaussian distribution
.. plot::
:context: close-figs
>>> az.plot_dist(b, rug=True)
Segment into quantiles
.. plot::
:context: close-figs
>>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75])
Plot as the cumulative distribution
.. plot::
:context: close-figs
>>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75], cumulative=True)
ArviZPythonPlots.plot_dist_comparison — Method
Plot to compare fitted and unfitted distributions.
The resulting plots will show the compared distributions both on
separate axes (particularly useful when one of them is substantially tighter
than another), and plotted together, displaying a grid of three plots per
distribution.
Parameters
----------
data : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
containing the posterior/prior data. Refer to documentation of
:func:`arviz.convert_to_dataset` for details.
kind : {"latent", "observed"}, default "latent"
kind of plot to display The "latent" option includes {"prior", "posterior"},
and the "observed" option includes
{"observed_data", "prior_predictive", "posterior_predictive"}.
figsize : (float, float), optional
Figure size. If ``None`` it will be defined automatically.
textsize : float
Text size scaling factor for labels, titles and lines. If ``None`` it will be
autoscaled based on `figsize`.
var_names : str, list, list of lists, optional
if str, plot the variable. if list, plot all the variables in list
of all groups. if list of lists, plot the vars of groups in respective lists.
See :ref:`this section <common_var_names>` for usage examples.
coords : dict
Dictionary mapping dimensions to selected coordinates to be plotted.
Dimensions without a mapping specified will include all coordinates for
that dimension. See :ref:`this section <common_coords>` for usage examples.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See :ref:`this section <common_combine_dims>` for usage examples.
transform : callable
Function to transform data (defaults to `None` i.e. the identity function).
legend : bool
Add legend to figure. By default True.
labeller : Labeller, optional
Class providing the method ``make_pp_label`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ax : (nvars, 3) array-like of matplotlib_axes, optional
Matplotlib axes: The ax argument should have shape (nvars, 3), where the
last column is for the combined before/after plots and columns 0 and 1 are
for the before and after plots, respectively.
prior_kwargs : dicts, optional
Additional keywords passed to :func:`arviz.plot_dist` for prior/predictive groups.
posterior_kwargs : dicts, optional
Additional keywords passed to :func:`arviz.plot_dist` for posterior/predictive groups.
observed_kwargs : dicts, optional
Additional keywords passed to :func:`arviz.plot_dist` for observed_data group.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : 2D ndarray of matplotlib_axes
Returned object will have shape (nvars, 3),
where the last column is the combined plot and the first columns are the single plots.
See Also
--------
plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive.
Examples
--------
Plot the prior/posterior plot for specified vars and coords.
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('rugby')
>>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]})
ArviZPythonPlots.plot_dot — Method
Plot distribution as dot plot or quantile dot plot.
This function uses the Wilkinson's Algorithm [1]_ to allot dots to bins.
The quantile dot plots was inspired from [2]_.
Parameters
----------
values : array-like
Values to plot from an unknown continuous or discrete distribution.
binwidth : float, optional
Width of the bin for drawing the dot plot.
dotsize : float, default 1
The size of the dots relative to the bin width. The default makes dots be
just about as wide as the bin width.
stackratio : float, default 1
The distance between the center of the dots in the same stack relative to the bin height.
The default makes dots in the same stack just touch each other.
point_interval : bool, default False
Plots the point interval. Uses ``hdi_prob`` to plot the HDI interval
point_estimate : str, optional
Plot point estimate per variable. Values should be ``mean``, ``median``, ``mode`` or None.
Defaults to ``auto`` i.e. it falls back to default set in rcParams.
dotcolor : string, optional
The color of the dots. Should be a valid matplotlib color.
intervalcolor : string, optional
The color of the interval. Should be a valid matplotlib color.
linewidth : int, default None
Line width throughout. If None it will be autoscaled based on `figsize`.
markersize : int, default None
Markersize throughout. If None it will be autoscaled based on `figsize`.
markercolor : string, optional
The color of the marker when plot_interval is True. Should be a valid matplotlib color.
marker : string, default "o"
The shape of the marker. Valid for matplotlib backend.
hdi_prob : float, optional
Valid only when point_interval is True. Plots HDI for chosen percentage of density.
Defaults to ``stats.ci_prob`` rcParam. See :ref:`this section <common_hdi_prob>`
for usage examples.
rotated : bool, default False
Whether to rotate the dot plot by 90 degrees.
nquantiles : int, default 50
Number of quantiles to plot, used for quantile dot plots.
quartiles : bool, default True
If True then the quartile interval will be plotted with the HDI.
figsize : (float,float), optional
Figure size. If ``None`` it will be defined automatically.
plot_kwargs : dict, optional
Keywords passed for customizing the dots. Passed to :class:`mpl:matplotlib.patches.Circle`
in matplotlib and :meth:`bokeh.plotting.figure.circle` in bokeh.
backend :{"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
ax : axes, optional
Matplotlib_axes or bokeh_figure.
show : bool, optional
Call backend show function.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
Returns
-------
axes : matplotlib_axes or bokeh_figure
See Also
--------
plot_dist : Plot distribution as histogram or kernel density estimates.
References
----------
.. [1] Leland Wilkinson (1999) Dot Plots, The American Statistician, 53:3, 276-281,
DOI: 10.1080/00031305.1999.10474474
.. [2] Matthew Kay, Tara Kola, Jessica R. Hullman,
and Sean A. Munson. 2016. When (ish) is My Bus? User-centered Visualizations of Uncertainty
in Everyday, Mobile Predictive Systems. DOI:https://doi.org/10.1145/2858036.2858558
Examples
--------
Plot dot plot for a set of data points
.. plot::
:context: close-figs
>>> import arviz as az
>>> import numpy as np
>>> values = np.random.normal(0, 1, 500)
>>> az.plot_dot(values)
Manually adjust number of quantiles to plot
.. plot::
:context: close-figs
>>> az.plot_dot(values, nquantiles=100)
Add a point interval under the dot plot
.. plot::
:context: close-figs
>>> az.plot_dot(values, point_interval=True)
Rotate the dot plots by 90 degrees i.e swap x and y axis
.. plot::
:context: close-figs
>>> az.plot_dot(values, point_interval=True, rotated=True)
ArviZPythonPlots.plot_ecdf — Method
Plot ECDF or ECDF-Difference Plot with Confidence bands.
Plots of the empirical cumulative distribution function (ECDF) of an array. Optionally, A `cdf`
argument representing a reference CDF may be provided for comparison using a difference ECDF
plot and/or confidence bands.
Alternatively, the PIT for a single dataset may be visualized.
Notes
-----
This plot computes the confidence bands with the simulated based algorithm presented in [1]_.
Parameters
----------
values : array-like
Values to plot from an unknown continuous or discrete distribution.
values2 : array-like, optional
values to compare to the original sample.
.. deprecated:: 0.18.0
Instead use ``cdf=scipy.stats.ecdf(values2).cdf.evaluate``.
cdf : callable, optional
Cumulative distribution function of the distribution to compare the original sample.
The function must take as input a numpy array of draws from the distribution.
difference : bool, default False
If True then plot ECDF-difference plot otherwise ECDF plot.
confidence_bands : str or bool
- False: No confidence bands are plotted (default).
- True: Plot bands computed with the default algorithm (subject to change)
- "pointwise": Compute the pointwise (i.e. marginal) confidence band.
- "optimized": Use optimization to estimate a simultaneous confidence band.
- "simulated": Use Monte Carlo simulation to estimate a simultaneous confidence
band.
For simultaneous confidence bands to be correctly calibrated, provide `eval_points` that
are not dependent on the `values`.
ci_prob : float, default 0.94
The probability that the true ECDF lies within the confidence band. If `confidence_bands`
is "pointwise", this is the marginal probability instead of the joint probability.
eval_points : array-like, optional
The points at which to evaluate the ECDF. If None, `npoints` uniformly spaced points
between the data bounds will be used.
rvs: callable, optional
A function that takes an integer `ndraws` and optionally the object passed to
`random_state` and returns an array of `ndraws` samples from the same distribution
as the original dataset. Required if `method` is "simulated" and variable is discrete.
random_state : int, numpy.random.Generator or numpy.random.RandomState, optional
num_trials : int, default 500
The number of random ECDFs to generate for constructing simultaneous confidence bands
(if `confidence_bands` is "simulated").
figsize : (float,float), optional
Figure size. If `None` it will be defined automatically.
fill_band : bool, default True
If True it fills in between to mark the area inside the confidence interval. Otherwise,
plot the border lines.
plot_kwargs : dict, optional
Additional kwargs passed to :func:`mpl:matplotlib.pyplot.step` or
:meth:`bokeh.plotting.figure.step`
fill_kwargs : dict, optional
Additional kwargs passed to :func:`mpl:matplotlib.pyplot.fill_between` or
:meth:`bokeh:bokeh.plotting.Figure.varea`
plot_outline_kwargs : dict, optional
Additional kwargs passed to :meth:`mpl:matplotlib.axes.Axes.plot` or
:meth:`bokeh:bokeh.plotting.Figure.line`
ax :axes, optional
Matplotlib axes or bokeh figures.
show : bool, optional
Call backend show function.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
npoints : int, default 100
The number of evaluation points for the ecdf or ecdf-difference plots, if `eval_points` is
not provided or `pit` is `True`.
.. deprecated:: 0.18.0
Instead specify ``eval_points=np.linspace(np.min(values), np.max(values), npoints)``
unless `pit` is `True`.
pointwise : bool, default False
.. deprecated:: 0.18.0
Instead use `confidence_bands="pointwise"`.
fpr : float, optional
.. deprecated:: 0.18.0
Instead use `ci_prob=1-fpr`.
pit : bool, default False
If True plots the ECDF or ECDF-diff of PIT of sample.
.. deprecated:: 0.18.0
See below example instead.
Returns
-------
axes : matplotlib_axes or bokeh_figure
References
----------
.. [1] Säilynoja, T., Bürkner, P.C. and Vehtari, A. (2022). Graphical Test for
Discrete Uniformity and its Applications in Goodness of Fit Evaluation and
Multiple Sample Comparison. Statistics and Computing, 32(32).
Examples
--------
In a future release, the default behaviour of ``plot_ecdf`` will change.
To maintain the original behaviour you should do:
.. plot::
:context: close-figs
>>> import arviz as az
>>> import numpy as np
>>> from scipy.stats import uniform, norm
>>>
>>> sample = norm(0,1).rvs(1000)
>>> npoints = 100
>>> az.plot_ecdf(sample, eval_points=np.linspace(sample.min(), sample.max(), npoints))
However, seeing this warning isn't an indicator of anything being wrong,
if you are happy to get different behaviour as ArviZ improves and adds
new algorithms you can ignore it like so:
.. plot::
:context: close-figs
>>> import warnings
>>> warnings.filterwarnings("ignore", category=az.utils.BehaviourChangeWarning)
Plot an ECDF plot for a given sample evaluated at the sample points. This will become
the new behaviour when `eval_points` is not provided:
.. plot::
:context: close-figs
>>> az.plot_ecdf(sample, eval_points=np.unique(sample))
Plot an ECDF plot with confidence bands for comparing a given sample to a given distribution.
We manually specify evaluation points independent of the values so that the confidence bands
are correctly calibrated.
.. plot::
:context: close-figs
>>> distribution = norm(0,1)
>>> eval_points = np.linspace(*distribution.ppf([0.001, 0.999]), 100)
>>> az.plot_ecdf(
>>> sample, eval_points=eval_points,
>>> cdf=distribution.cdf, confidence_bands=True
>>> )
Plot an ECDF-difference plot with confidence bands for comparing a given sample
to a given distribution.
.. plot::
:context: close-figs
>>> az.plot_ecdf(
>>> sample, cdf=distribution.cdf,
>>> confidence_bands=True, difference=True
>>> )
Plot an ECDF plot with confidence bands for the probability integral transform (PIT) of a
continuous sample. If drawn from the reference distribution, the PIT values should be uniformly
distributed.
.. plot::
:context: close-figs
>>> pit_vals = distribution.cdf(sample)
>>> uniform_dist = uniform(0, 1)
>>> az.plot_ecdf(
>>> pit_vals, cdf=uniform_dist.cdf, confidence_bands=True,
>>> )
Plot an ECDF-difference plot of PIT values.
.. plot::
:context: close-figs
>>> az.plot_ecdf(
>>> pit_vals, cdf = uniform_dist.cdf, confidence_bands = True,
>>> difference = True
>>> )
ArviZPythonPlots.plot_elpd — Method
Plot pointwise elpd differences between two or more models.
Pointwise model comparison based on their expected log pointwise predictive density (ELPD).
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]_.
Parameters
----------
compare_dict : mapping of {str : ELPDData or InferenceData}
A dictionary mapping the model name to the object containing inference data or the result
of :func:`arviz.loo` or :func:`arviz.waic` functions.
Refer to :func:`arviz.convert_to_inference_data` for details on possible dict items.
color : str 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.
xlabels : bool, default False
Use coords as xticklabels.
figsize : (float, float), optional
If `None`, size is (8 + numvars, 8 + numvars).
textsize : float, optional
Text size for labels. If `None` it will be autoscaled based on `figsize`.
coords : mapping, optional
Coordinates of points to plot. **All** values are used for computation, but only a
subset can be plotted for convenience. See :ref:`this section <common_coords>`
for usage examples.
legend : bool, default False
Include a legend to the plot. Only taken into account when color argument is a dim name.
threshold : float, optional
If some elpd difference is larger than ``threshold * elpd.std()``, show its label. If
`None`, no observations will be highlighted.
ic : str, 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 :class:`arviz.InferenceData`.
scale : str, optional
Scale argument passed to :func:`arviz.loo` or :func:`arviz.waic`, see their docs for
details. Only taken into account when values in ``compare_dict`` are
:class:`arviz.InferenceData`.
var_name : str, optional
Argument passed to to :func:`arviz.loo` or :func:`arviz.waic`, see their docs for
details. Only taken into account when values in ``compare_dict`` are
:class:`arviz.InferenceData`.
plot_kwargs : dicts, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter`.
ax : axes, optional
:class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib_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.
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.
.. plot::
:context: close-figs
>>> 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
>>> )
.. bokeh-plot::
:source-position: above
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"
)
ArviZPythonPlots.plot_energy — Method
Plot energy transition distribution and marginal energy distribution in HMC algorithms.
This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.
The energy function in HMC can identify posteriors with heavy tailed distributions, that
in practice are challenging for sampling.
This plot is in the style of the one used in [1]_.
Parameters
----------
data : obj
:class:`xarray.Dataset`, or any object that can be converted (must represent
``sample_stats`` and have an ``energy`` variable).
kind : str, optional
Type of plot to display ("kde", "hist").
bfmi : bool, default True
If True add to the plot the value of the estimated Bayesian fraction of missing
information.
figsize : (float, float), optional
Figure size. If `None` it will be defined automatically.
legend : bool, default True
Flag for plotting legend.
fill_alpha : tuple, default (1, 0.75)
Alpha blending value for the shaded area under the curve, between 0
(no shade) and 1 (opaque).
fill_color : tuple of valid matplotlib color, default ('C0', 'C5')
Color for Marginal energy distribution and Energy transition distribution.
bw : float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental". Defaults to "experimental".
Only works if ``kind='kde'``.
textsize : float, optional
Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled
based on `figsize`.
fill_kwargs : dicts, optional
Additional keywords passed to :func:`arviz.plot_kde` (to control the shade).
plot_kwargs : dicts, optional
Additional keywords passed to :func:`arviz.plot_kde` or :func:`matplotlib.pyplot.hist`
(if ``type='hist'``).
ax : axes, optional
:class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib axes or bokeh figures
See Also
--------
bfmi : Calculate the estimated Bayesian fraction of missing information (BFMI).
References
----------
.. [1] Betancourt (2016). Diagnosing Suboptimal Cotangent Disintegrations in
Hamiltonian Monte Carlo https://arxiv.org/abs/1604.00695
Examples
--------
Plot a default energy plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_energy(data)
Represent energy plot via histograms
.. plot::
:context: close-figs
>>> az.plot_energy(data, kind='hist')
ArviZPythonPlots.plot_ess — Method
Generate quantile, local, or evolution ESS plots.
The local and the quantile ESS plots are recommended for checking
that there are enough samples for all the explored regions of the
parameter space. Checking local and quantile ESS is particularly
relevant when working with HDI intervals as opposed to ESS bulk,
which is suitable for point estimates.
Parameters
----------
idata : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details.
var_names : list of str, optional
Variables to be plotted. Prefix the variables by ``~`` when you want to exclude
them from the plot. See :ref:`this section <common_var_names>` for usage examples.
filter_vars : {None, "like", "regex"}, 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. See
:ref:`this section <common_filter_vars>` for usage examples.
kind : {"local", "quantile", "evolution"}, default "local"
Specify the kind of plot:
* The ``kind="local"`` argument generates the ESS' local efficiency for
estimating quantiles of a desired posterior.
* The ``kind="quantile"`` argument generates the ESS' local efficiency
for estimating small-interval probability of a desired posterior.
* The ``kind="evolution"`` argument generates the estimated ESS'
with incrised number of iterations of a desired posterior.
relative : bool, default False
Show relative ess in plot ``ress = ess / N``.
coords : dict, optional
Coordinates of `var_names` to be plotted. Passed to :meth:`xarray.Dataset.sel`.
See :ref:`this section <common_coords>` for usage examples.
grid : tuple, optional
Number of rows and columns. By default, the rows and columns are
automatically inferred. See :ref:`this section <common_grid>` for usage examples.
figsize : (float, float), 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`.
rug : bool, default False
Add a `rug plot <https://en.wikipedia.org/wiki/Rug_plot>`_ for a specific subset of values.
rug_kind : str, default "diverging"
Variable in sample stats to use as rug mask. Must be a boolean variable.
n_points : int, default 20
Number of points for which to plot their quantile/local ess or number of subsets
in the evolution plot.
extra_methods : bool, default False
Plot mean and sd ESS as horizontal lines. Not taken into account if ``kind = 'evolution'``.
min_ess : int, default 400
Minimum number of ESS desired. If ``relative=True`` the line is plotted at
``min_ess / n_samples`` for local and quantile kinds and as a curve following
the ``min_ess / n`` dependency in evolution kind.
labeller : Labeller, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ax : 2D array-like of matplotlib_axes or bokeh_figure, 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).
extra_kwargs : dict, optional
If evolution plot, `extra_kwargs` is used to plot ess tail and differentiate it
from ess bulk. Otherwise, passed to extra methods lines.
text_kwargs : dict, optional
Only taken into account when ``extra_methods=True``. kwargs passed to ax.annotate
for extra methods lines labels. It accepts the additional
key ``x`` to set ``xy=(text_kwargs["x"], mcse)``
hline_kwargs : dict, optional
kwargs passed to :func:`~matplotlib.axes.Axes.axhline` or to :class:`~bokeh.models.Span`
depending on the backend for the horizontal minimum ESS line.
For relative ess evolution plots the kwargs are passed to
:func:`~matplotlib.axes.Axes.plot` or to :class:`~bokeh.plotting.figure.line`
rug_kwargs : dict
kwargs passed to rug plot.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
**kwargs
Passed as-is to :meth:`mpl:matplotlib.axes.Axes.hist` or
:meth:`mpl:matplotlib.axes.Axes.plot` function depending on the
value of `kind`.
Returns
-------
axes : matplotlib_axes or bokeh_figure
See Also
--------
ess : Calculate estimate of the effective sample size.
References
----------
.. [1] Vehtari et al. (2021). Rank-normalization, folding, and
localization: An improved Rhat for assessing convergence of
MCMC. Bayesian analysis, 16(2):667-718.
Examples
--------
Plot local ESS.
.. plot::
:context: close-figs
>>> import arviz as az
>>> idata = az.load_arviz_data("centered_eight")
>>> coords = {"school": ["Choate", "Lawrenceville"]}
>>> az.plot_ess(
... idata, kind="local", var_names=["mu", "theta"], coords=coords
... )
Plot ESS evolution as the number of samples increase. When the model is converging properly,
both lines in this plot should be roughly linear.
.. plot::
:context: close-figs
>>> az.plot_ess(
... idata, kind="evolution", var_names=["mu", "theta"], coords=coords
... )
Customize local ESS plot to look like reference paper.
.. plot::
:context: close-figs
>>> az.plot_ess(
... idata, kind="local", var_names=["mu"], drawstyle="steps-mid", color="k",
... linestyle="-", marker=None, rug=True, rug_kwargs={"color": "r"}
... )
Customize ESS evolution plot to look like reference paper.
.. plot::
:context: close-figs
>>> extra_kwargs = {"color": "lightsteelblue"}
>>> az.plot_ess(
... idata, kind="evolution", var_names=["mu"],
... color="royalblue", extra_kwargs=extra_kwargs
... )
ArviZPythonPlots.plot_forest — Method
Forest plot to compare HDI intervals from a number of distributions.
Generate forest or ridge plots to compare distributions from a model or list of models.
Additionally, the function can display effective sample sizes (ess) and Rhats to visualize
convergence diagnostics alongside the distributions.
Parameters
----------
data : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details.
kind : {"foresplot", "ridgeplot"}, default "forestplot"
Specify the kind of plot:
* The ``kind="forestplot"`` generates credible intervals, where the central points are the
estimated posterior median, the thick lines are the central quartiles, and the thin lines
represent the :math:`100\times(hdi\_prob)\%` highest density intervals.
* The ``kind="ridgeplot"`` option generates density plots (kernel density estimate or
histograms) in the same graph. Ridge plots can be configured to have different overlap,
truncation bounds and quantile markers.
model_names : list of str, optional
List with names for the models in the list of data. Useful when plotting more that one
dataset.
var_names : list of str, optional
Variables to be plotted. Prefix the variables by ``~`` when you want to exclude
them from the plot. See :ref:`this section <common_var_names>` for usage examples.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See :ref:`this section <common_combine_dims>` for usage examples.
filter_vars : {None, "like", "regex"}, 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. See
:ref:`this section <common_filter_vars>` for usage examples.
transform : callable, optional
Function to transform data (defaults to None i.e.the identity function).
coords : dict, optional
Coordinates of ``var_names`` to be plotted. Passed to :meth:`xarray.Dataset.sel`.
See :ref:`this section <common_coords>` for usage examples.
combined : bool, default False
Flag for combining multiple chains into a single chain. If False, chains will
be plotted separately. See :ref:`this section <common_combine>` for usage examples.
hdi_prob : float, default 0.94
Plots highest posterior density interval for chosen percentage of density.
See :ref:`this section <common_ hdi_prob>` for usage examples.
rope : list, tuple or dictionary of {str : tuples or lists}, optional
A dictionary of tuples with the lower and upper values of the Region Of Practical
Equivalence. See :ref:`this section <common_rope>` for usage examples.
quartiles : bool, default True
Flag for plotting the interquartile range, in addition to the ``hdi_prob`` intervals.
r_hat : bool, default False
Flag for plotting Split R-hat statistics. Requires 2 or more chains.
ess : bool, default False
Flag for plotting the effective sample size.
colors : list or string, optional
list with valid matplotlib colors, one color per model. Alternative a string can be passed.
If the string is `cycle`, it will automatically chose a color per model from the matplotlibs
cycle. If a single color is passed, eg 'k', 'C2', 'red' this color will be used for all
models. Defaults to 'cycle'.
textsize : float, optional
Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based
on ``figsize``.
linewidth : int, optional
Line width throughout. If `None` it will be autoscaled based on ``figsize``.
markersize : int, optional
Markersize throughout. If `None` it will be autoscaled based on ``figsize``.
legend : bool, optional
Show a legend with the color encoded model information.
Defaults to True, if there are multiple models.
labeller : Labeller, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ridgeplot_alpha: float, optional
Transparency for ridgeplot fill. If ``ridgeplot_alpha=0``, border is colored by model,
otherwise a `black` outline is used.
ridgeplot_overlap : float, default 2
Overlap height for ridgeplots.
ridgeplot_kind : string, optional
By default ("auto") continuous variables are plotted using KDEs and discrete ones using
histograms. To override this use "hist" to plot histograms and "density" for KDEs.
ridgeplot_truncate : bool, default True
Whether to truncate densities according to the value of ``hdi_prob``.
ridgeplot_quantiles : list, optional
Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles.
figsize : (float, float), optional
Figure size. If `None`, it will be defined automatically.
ax : axes, optional
:class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
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
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
1D ndarray of matplotlib_axes or bokeh_figures
See Also
--------
plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book.
plot_density : Generate KDE plots for continuous variables and histograms for discrete ones.
summary : Create a data frame with summary statistics.
Examples
--------
Forestplot
.. plot::
:context: close-figs
>>> import arviz as az
>>> non_centered_data = az.load_arviz_data('non_centered_eight')
>>> axes = az.plot_forest(non_centered_data,
>>> kind='forestplot',
>>> var_names=["^the"],
>>> filter_vars="regex",
>>> combined=True,
>>> figsize=(9, 7))
>>> axes[0].set_title('Estimated theta for 8 schools model')
Forestplot with multiple datasets
.. plot::
:context: close-figs
>>> centered_data = az.load_arviz_data('centered_eight')
>>> axes = az.plot_forest([non_centered_data, centered_data],
>>> model_names = ["non centered eight", "centered eight"],
>>> kind='forestplot',
>>> var_names=["^the"],
>>> filter_vars="regex",
>>> combined=True,
>>> figsize=(9, 7))
>>> axes[0].set_title('Estimated theta for 8 schools models')
Ridgeplot
.. plot::
:context: close-figs
>>> axes = az.plot_forest(non_centered_data,
>>> kind='ridgeplot',
>>> var_names=['theta'],
>>> combined=True,
>>> ridgeplot_overlap=3,
>>> colors='white',
>>> figsize=(9, 7))
>>> axes[0].set_title('Estimated theta for 8 schools model')
Ridgeplot non-truncated and with quantiles
.. plot::
:context: close-figs
>>> axes = az.plot_forest(non_centered_data,
>>> kind='ridgeplot',
>>> var_names=['theta'],
>>> combined=True,
>>> ridgeplot_truncate=False,
>>> ridgeplot_quantiles=[.25, .5, .75],
>>> ridgeplot_overlap=0.7,
>>> colors='white',
>>> figsize=(9, 7))
>>> axes[0].set_title('Estimated theta for 8 schools model')
ArviZPythonPlots.plot_hdi — Method
Plot HDI intervals for regression data.
Parameters
----------
x : array-like
Values to plot.
y : array-like, optional
Values from which to compute the HDI. Assumed shape ``(chain, draw, \*shape)``.
Only optional if ``hdi_data`` is present.
hdi_data : array_like, optional
Precomputed HDI values to use. Assumed shape is ``(*x.shape, 2)``.
hdi_prob : float, optional
Probability for the highest density interval. Defaults to ``stats.ci_prob`` rcParam.
See :ref:`this section <common_ hdi_prob>` for usage examples.
color : str, default "C1"
Color used for the limits of the HDI and fill. Should be a valid matplotlib color.
circular : bool, default False
Whether to compute the HDI taking into account ``x`` is a circular variable
(in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).
smooth : boolean, default True
If True the result will be smoothed by first computing a linear interpolation of the data
over a regular grid and then applying the Savitzky-Golay filter to the interpolated data.
smooth_kwargs : dict, optional
Additional keywords modifying the Savitzky-Golay filter. See
:func:`scipy:scipy.signal.savgol_filter` for details.
figsize : (float, float), optional
Figure size. If ``None``, it will be defined automatically.
fill_kwargs : dict, optional
Keywords passed to :meth:`mpl:matplotlib.axes.Axes.fill_between`
(use ``fill_kwargs={'alpha': 0}`` to disable fill) or to
:meth:`bokeh.plotting.Figure.patch`.
plot_kwargs : dict, optional
HDI limits keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or
:meth:`bokeh.plotting.Figure.patch`.
hdi_kwargs : dict, optional
Keyword arguments passed to :func:`~arviz.hdi`. Ignored if ``hdi_data`` is present.
ax : axes, optional
Matplotlib axes or bokeh figures.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib axes or bokeh figures
See Also
--------
hdi : Calculate highest density interval (HDI) of array for given probability.
Examples
--------
Plot HDI interval of simulated random-walk data using `y` argument:
.. plot::
:context: close-figs
>>> import numpy as np
>>> import arviz as az
>>> # time-steps random walk
>>> x_data =np.arange(0,100)
>>> # Mean random walk
>>> mu = np.zeros(100)
>>> for i in x_data: mu[i] = mu[i-1] + np.random.normal(0, 1, 1)
>>> # Simulated pp samples form the random walk time series
>>> y_data = np.random.normal(2 + mu * 0.5, 0.5, size = (2, 50, 100))
>>> az.plot_hdi(x_data, y_data)
``plot_hdi`` can also be given precalculated values with the argument ``hdi_data``. This example
shows how to use :func:`~arviz.hdi` to precalculate the values and pass these values to
``plot_hdi``. Similarly to an example in ``hdi`` we are using the ``input_core_dims``
argument of :func:`~arviz.wrap_xarray_ufunc` to manually define the dimensions over which
to calculate the HDI.
.. plot::
:context: close-figs
>>> hdi_data = az.hdi(y_data, input_core_dims=[["draw"]])
>>> ax = az.plot_hdi(x_data, hdi_data=hdi_data[0], color="r", fill_kwargs={"alpha": .2})
>>> az.plot_hdi(x_data, hdi_data=hdi_data[1], color="k", ax=ax, fill_kwargs={"alpha": .2})
``plot_hdi`` can also be used with Inference Data objects. Here we use the posterior predictive
to plot the HDI interval.
.. plot::
:context: close-figs
>>> X = np.random.normal(0,1,100)
>>> Y = np.random.normal(2 + X * 0.5, 0.5, size=(2,10,100))
>>> idata = az.from_dict(posterior={"y": Y}, constant_data={"x":X})
>>> x_data = idata.constant_data.x
>>> y_data = idata.posterior.y
>>> az.plot_hdi(x_data, y_data)
ArviZPythonPlots.plot_kde — Method
1D or 2D KDE plot taking into account boundary conditions.
Parameters
----------
values : array-like
Values to plot
values2 : array-like, optional
Values to plot. If present, a 2D KDE will be estimated
cumulative : bool, dafault False
If True plot the estimated cumulative distribution function. Ignored for 2D KDE.
rug : bool, default False
Add a `rug plot <https://en.wikipedia.org/wiki/Rug_plot>`_ for a specific subset of
values. Ignored for 2D KDE.
label : string, optional
Text to include as part of the legend.
bw : float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental" when ``is_circular`` is False
and "taylor" (for now) when ``is_circular`` is True.
Defaults to "default" which means "experimental" when variable is not circular
and "taylor" when it is.
adaptive : bool, default False
If True, an adaptative bandwidth is used. Only valid for 1D KDE.
quantiles : list, optional
Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles.
rotated : bool, default False
Whether to rotate the 1D KDE plot 90 degrees.
contour : bool, default True
If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE.
hdi_probs : list, optional
Plots highest density credibility regions for the provided probabilities for a 2D KDE.
Defaults to [0.5, 0.8, 0.94].
fill_last : bool, default False
If True fill the last contour of the 2D KDE plot.
figsize : (float, float), 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``. Not implemented for bokeh backend.
plot_kwargs : dict, optional
Keywords passed to the pdf line of a 1D KDE. See :meth:`mpl:matplotlib.axes.Axes.plot`
or :meth:`bokeh:bokeh.plotting.Figure.line` for a description of accepted values.
fill_kwargs : dict, optional
Keywords passed to the fill under the line (use ``fill_kwargs={'alpha': 0}``
to disable fill). Ignored for 2D KDE. Passed to
:meth:`bokeh.plotting.Figure.patch`.
rug_kwargs : dict, optional
Keywords passed to the rug plot. Ignored if ``rug=False`` or for 2D KDE
Use ``space`` keyword (float) to control the position of the rugplot. The larger this number
the lower the rugplot. Passed to :class:`bokeh:bokeh.models.glyphs.Scatter`.
contour_kwargs : dict, optional
Keywords passed to :meth:`mpl:matplotlib.axes.Axes.contour`
to draw contour lines or :meth:`bokeh.plotting.Figure.patch`.
Ignored for 1D KDE.
contourf_kwargs : dict, optional
Keywords passed to :meth:`mpl:matplotlib.axes.Axes.contourf`
to draw filled contours. Ignored for 1D KDE.
pcolormesh_kwargs : dict, optional
Keywords passed to :meth:`mpl:matplotlib.axes.Axes.pcolormesh` or
:meth:`bokeh.plotting.Figure.image`.
Ignored for 1D KDE.
is_circular : {False, True, "radians", "degrees"}. Default False
Select input type {"radians", "degrees"} for circular histogram or KDE plot. If True,
default input type is "radians". When this argument is present, it interprets ``values``
as a circular variable measured in radians and a circular KDE is used. Inputs in
"degrees" will undergo an internal conversion to radians.
ax : axes, optional
Matplotlib axes or bokeh figures.
legend : bool, default True
Add legend to the figure.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
return_glyph : bool, optional
Internal argument to return glyphs for bokeh.
Returns
-------
axes : matplotlib.Axes or bokeh.plotting.Figure
Object containing the kde plot
glyphs : list, optional
Bokeh glyphs present in plot. Only provided if ``return_glyph`` is True.
See Also
--------
kde : One dimensional density estimation.
plot_dist : Plot distribution as histogram or kernel density estimates.
Examples
--------
Plot default KDE
.. plot::
:context: close-figs
>>> import arviz as az
>>> non_centered = az.load_arviz_data('non_centered_eight')
>>> mu_posterior = np.concatenate(non_centered.posterior["mu"].values)
>>> tau_posterior = np.concatenate(non_centered.posterior["tau"].values)
>>> az.plot_kde(mu_posterior)
Plot KDE with rugplot
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, rug=True)
Plot KDE with adaptive bandwidth
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, adaptive=True)
Plot KDE with a different bandwidth estimator
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, bw="scott")
Plot KDE with a bandwidth specified manually
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, bw=0.4)
Plot KDE for a circular variable
.. plot::
:context: close-figs
>>> rvs = np.random.vonmises(mu=np.pi, kappa=2, size=500)
>>> az.plot_kde(rvs, is_circular=True)
Plot a cumulative distribution
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, cumulative=True)
Rotate plot 90 degrees
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, rotated=True)
Plot 2d contour KDE
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, values2=tau_posterior)
Plot 2d contour KDE, without filling and contour lines using viridis cmap
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, values2=tau_posterior,
... contour_kwargs={"colors":None, "cmap":plt.cm.viridis},
... contourf_kwargs={"alpha":0});
Plot 2d contour KDE, set the number of levels to 3.
.. plot::
:context: close-figs
>>> az.plot_kde(
... mu_posterior, values2=tau_posterior,
... contour_kwargs={"levels":3}, contourf_kwargs={"levels":3}
... );
Plot 2d contour KDE with 30%, 60% and 90% HDI contours.
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, values2=tau_posterior, hdi_probs=[0.3, 0.6, 0.9])
Plot 2d smooth KDE
.. plot::
:context: close-figs
>>> az.plot_kde(mu_posterior, values2=tau_posterior, contour=False)
ArviZPythonPlots.plot_khat — Method
Plot Pareto tail indices :math:`\hat{k}` for diagnosing convergence in PSIS-LOO.
Parameters
----------
khats : ELPDData
The input Pareto tail indices to be plotted.
color : str 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. If Matplotlib c argument is passed, it will override the color argument.
xlabels : bool, default False
Use coords as xticklabels.
show_hlines : bool, default False
Show the horizontal lines, by default at the values [0, 0.5, 0.7, 1].
show_bins : bool, default False
Show the percentage of khats falling in each bin, as delimited by hlines.
bin_format : str, optional
The string is used as formatting guide calling ``bin_format.format(count, pct)``.
threshold : float, optional
Show the labels of k values larger than `threshold`. If ``None`` (default), no
observations will be highlighted.
hover_label : bool, default False
Show the datapoint label when hovering over it with the mouse. Requires an interactive
backend.
hover_format : str, default "{1}"
String used to format the hover label via ``hover_format.format(idx, coord_label)``
figsize : (float, float), 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`.
coords : mapping, optional
Coordinates of points to plot. **All** values are used for computation, but only a
a subset can be plotted for convenience. See :ref:`this section <common_coords>` for
usage examples.
legend : bool, default False
Include a legend to the plot. Only taken into account when color argument is a dim name.
markersize : int, optional
markersize for scatter plot. Defaults to ``None`` in which case it will
be chosen based on autoscaling for figsize.
ax : axes, optional
Matplotlib axes or bokeh figures.
hlines_kwargs : dict, optional
Additional keywords passed to
:meth:`matplotlib.axes.Axes.hlines`.
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
kwargs :
Additional keywords passed to
:meth:`matplotlib.axes.Axes.scatter`.
Returns
-------
axes : matplotlib_axes or bokeh_figures
See Also
--------
psislw : Pareto smoothed importance sampling (PSIS).
Examples
--------
Plot estimated pareto shape parameters showing how many fall in each category.
.. plot::
:context: close-figs
>>> import arviz as az
>>> radon = az.load_arviz_data("radon")
>>> loo_radon = az.loo(radon, pointwise=True)
>>> az.plot_khat(loo_radon, show_bins=True)
Show xlabels
.. plot::
:context: close-figs
>>> centered_eight = az.load_arviz_data("centered_eight")
>>> khats = az.loo(centered_eight, pointwise=True).pareto_k
>>> az.plot_khat(khats, xlabels=True, threshold=1)
Use custom color scheme
.. plot::
:context: close-figs
>>> counties = radon.posterior.County[radon.constant_data.county_idx].values
>>> colors = [
... "blue" if county[-1] in ("A", "N") else "green" for county in counties
... ]
>>> az.plot_khat(loo_radon, color=colors)
Notes
-----
The Generalized Pareto distribution (GPD) diagnoses convergence rates for importance
sampling. GPD has parameters offset, scale, and shape. The shape parameter (:math:`k`)
tells the distribution's number of finite moments. The pre-asymptotic convergence rate
of importance sampling can be estimated based on the fractional number of finite moments
of the importance ratio distribution. GPD is fitted to the largest importance ratios and
interprets the estimated shape parameter :math:`k`, i.e., :math:`\hat{k}` can then be
used as a diagnostic (most importantly if :math:`\hat{k} > 0.7`, then the convergence
rate is impractically low). See [1]_.
References
----------
.. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J. (2024).
Pareto Smoothed Importance Sampling. Journal of Machine Learning
Research, 25(72):1-58.
ArviZPythonPlots.plot_lm — Method
Posterior predictive and mean plots for regression-like data.
Parameters
----------
y : str or DataArray or ndarray
If str, variable name from ``observed_data``.
idata : InferenceData, Optional
Optional only if ``y`` is not str.
x : str, tuple of strings, DataArray or array-like, optional
If str or tuple, variable name from ``constant_data``.
If ndarray, could be 1D, or 2D for multiple plots.
If None, coords name of ``y`` (``y`` should be DataArray).
y_model : str or Sequence, Optional
If str, variable name from ``posterior``.
Its dimensions should be same as ``y`` plus added chains and draws.
y_hat : str, Optional
If str, variable name from ``posterior_predictive``.
Its dimensions should be same as ``y`` plus added chains and draws.
num_samples : int, Optional, Default 50
Significant if ``kind_pp`` is "samples" or ``kind_model`` is "lines".
Number of samples to be drawn from posterior predictive or
kind_pp : {"samples", "hdi"}, Default "samples"
Options to visualize uncertainty in data.
kind_model : {"lines", "hdi"}, Default "lines"
Options to visualize uncertainty in mean of the data.
plot_dim : str, Optional
Necessary if ``y`` is multidimensional.
backend : str, Optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
y_kwargs : dict, optional
Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib
and :meth:`bokeh:bokeh.plotting.Figure.circle` in bokeh
y_hat_plot_kwargs : dict, optional
Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib
and :meth:`bokeh:bokeh.plotting.Figure.circle` in bokeh
y_hat_fill_kwargs : dict, optional
Passed to :func:`arviz.plot_hdi`
y_model_plot_kwargs : dict, optional
Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib
and :meth:`bokeh:bokeh.plotting.Figure.line` in bokeh
y_model_fill_kwargs : dict, optional
Significant if ``kind_model`` is "hdi". Passed to :func:`arviz.plot_hdi`
y_model_mean_kwargs : dict, optional
Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib
and :meth:`bokeh:bokeh.plotting.Figure.line` in bokeh
backend_kwargs : dict, optional
These are kwargs specific to the backend being used. Passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`.
figsize : (float, float), 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``.
axes : 2D 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).
show : bool, optional
Call backend show function.
legend : bool, optional
Add legend to figure. By default True.
grid : bool, optional
Add grid to figure. By default True.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_ts : Plot timeseries data
plot_ppc : Plot for posterior/prior predictive checks
Examples
--------
Plot regression default plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> import numpy as np
>>> import xarray as xr
>>> idata = az.load_arviz_data('regression1d')
>>> x = xr.DataArray(np.linspace(0, 1, 100))
>>> idata.posterior["y_model"] = idata.posterior["intercept"] + idata.posterior["slope"]*x
>>> az.plot_lm(idata=idata, y="y", x=x)
Plot regression data and mean uncertainty
.. plot::
:context: close-figs
>>> az.plot_lm(idata=idata, y="y", x=x, y_model="y_model")
Plot regression data and mean uncertainty in hdi form
.. plot::
:context: close-figs
>>> az.plot_lm(
... idata=idata, y="y", x=x, y_model="y_model", kind_pp="hdi", kind_model="hdi"
... )
Plot regression data for multi-dimensional y using plot_dim
.. plot::
:context: close-figs
>>> data = az.from_dict(
... observed_data = { "y": np.random.normal(size=(5, 7)) },
... posterior_predictive = {"y": np.random.randn(4, 1000, 5, 7) / 2},
... dims={"y": ["dim1", "dim2"]},
... coords={"dim1": range(5), "dim2": range(7)}
... )
>>> az.plot_lm(idata=data, y="y", plot_dim="dim1")
ArviZPythonPlots.plot_loo_pit — Method
Plot Leave-One-Out (LOO) probability integral transformation (PIT) predictive checks.
Parameters
----------
idata : InferenceData
:class:`arviz.InferenceData` object.
y : array, DataArray or str
Observed data. If str, ``idata`` must be present and contain the observed data group
y_hat : array, DataArray or str
Posterior predictive samples for ``y``. It must have the same shape as y plus an
extra dimension at the end of size n_samples (chains and draws stacked). If str or
None, ``idata`` must contain the posterior predictive group. If None, ``y_hat`` is taken
equal to y, thus, y must be str too.
log_weights : array or DataArray
Smoothed log_weights. It must have the same shape as ``y_hat``
ecdf : bool, optional
Plot the difference between the LOO-PIT Empirical Cumulative Distribution Function
(ECDF) and the uniform CDF instead of LOO-PIT kde.
In this case, instead of overlaying uniform distributions, the beta ``hdi_prob``
around the theoretical uniform CDF is shown. This approximation only holds
for large S and ECDF values not very close to 0 nor 1. For more information, see
`Vehtari et al. (2021)`, `Appendix G <https://avehtari.github.io/rhat_ess/rhat_ess.html>`_.
ecdf_fill : bool, optional
Use :meth:`matplotlib.axes.Axes.fill_between` to mark the area
inside the credible interval. Otherwise, plot the
border lines.
n_unif : int, optional
Number of datasets to simulate and overlay from the uniform distribution.
use_hdi : bool, optional
Compute expected hdi values instead of overlaying the sampled uniform distributions.
hdi_prob : float, optional
Probability for the highest density interval. Works with ``use_hdi=True`` or ``ecdf=True``.
figsize : (float, float), 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``.
labeller : Labeller, optional
Class providing the method ``make_pp_label`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
color : str or array_like, optional
Color of the LOO-PIT estimated pdf plot. If ``plot_unif_kwargs`` has no "color" key,
a slightly lighter color than this argument will be used for the uniform kde lines.
This will ensure that LOO-PIT kde and uniform kde have different default colors.
legend : bool, optional
Show the legend of the figure.
ax : axes, optional
Matplotlib axes or bokeh figures.
plot_kwargs : dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.plot`
for LOO-PIT line (kde or ECDF)
plot_unif_kwargs : dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.plot` for
overlaid uniform distributions or for beta credible interval
lines if ``ecdf=True``
hdi_kwargs : dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.axhspan`
fill_kwargs : dict, optional
Additional kwargs passed to :meth:`matplotlib.axes.Axes.fill_between`
backend : str, optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
backend_kwargs : bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`. For additional documentation
check the plotting method of the backend.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib_axes or bokeh_figures
See Also
--------
plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive.
loo_pit : Compute leave one out (PSIS-LOO) probability integral transform (PIT) values.
References
----------
* Gabry et al. (2017) see https://arxiv.org/abs/1709.01449
* https://mc-stan.org/bayesplot/reference/PPC-loo.html
* Gelman et al. BDA (2014) Section 6.3
Examples
--------
Plot LOO-PIT predictive checks overlaying the KDE of the LOO-PIT values to several
realizations of uniform variable sampling with the same number of observations.
.. plot::
:context: close-figs
>>> import arviz as az
>>> idata = az.load_arviz_data("radon")
>>> az.plot_loo_pit(idata=idata, y="y")
Fill the area containing the 94% highest density interval of the difference between uniform
variables empirical CDF and the real uniform CDF. A LOO-PIT ECDF clearly outside of these
theoretical boundaries indicates that the observations and the posterior predictive
samples do not follow the same distribution.
.. plot::
:context: close-figs
>>> az.plot_loo_pit(idata=idata, y="y", ecdf=True)
ArviZPythonPlots.plot_mcse — Method
Plot quantile or local Monte Carlo Standard Error.
Parameters
----------
idata : obj
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`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 :meth:`xarray.Dataset.sel`
errorbar : bool, optional
Plot quantile value +/- mcse instead of plotting mcse.
grid : tuple
Number of rows and columns. Defaults to None, the rows and columns are
automatically inferred.
figsize : (float, float), 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.
labeller : Labeller, optional
Class providing the method `make_label_vert` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
ax : 2D 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
:meth:`mpl:matplotlib.axes.Axes.plot` or :class:`bokeh:bokeh.models.glyphs.Scatter`.
extra_kwargs : dict, optional
kwargs passed as extra method lines in
:meth:`mpl:matplotlib.axes.Axes.axhline` or :class:`bokeh:bokeh.models.Span`
text_kwargs : dict, optional
kwargs passed to :meth:`mpl: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
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
show: bool, optional
Call backend show function.
**kwargs
Passed as-is to :meth:`mpl:matplotlib.axes.Axes.hist` or
:meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib depending on the value of `kind`.
Returns
-------
axes : matplotlib axes or bokeh figures
See Also
--------
:func:`arviz.mcse`: Calculate Markov Chain Standard Error statistic.
References
----------
.. [1] Vehtari et al. (2021). Rank-normalization, folding, and
localization: An improved Rhat for assessing convergence of
MCMC. Bayesian analysis, 16(2):667-718.
Examples
--------
Plot quantile Monte Carlo Standard Error.
.. plot::
:context: close-figs
>>> 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
... )
ArviZPythonPlots.plot_pair — Method
Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal.
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object.
Refer to documentation of :func:`arviz.convert_to_dataset` for details
group: str, optional
Specifies which InferenceData group should be plotted. Defaults to 'posterior'.
var_names: list of variable names, optional
Variables to be plotted, if None all variable are 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``.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See the :ref:`this section <common_combine_dims>` for usage examples.
coords: mapping, optional
Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`.
marginals: bool, optional
If True pairplot will include marginal distributions for every variable
figsize: figure size tuple
If None, size is (8 + numvars, 8 + numvars)
textsize: int
Text size for labels. If None it will be autoscaled based on ``figsize``.
kind : str or List[str]
Type of plot to display (scatter, kde and/or hexbin)
gridsize: int or (int, int), optional
Only works for ``kind=hexbin``. The number of hexagons in the x-direction.
The corresponding number of hexagons in the y-direction is chosen
such that the hexagons are approximately regular. Alternatively, gridsize
can be a tuple with two elements specifying the number of hexagons
in the x-direction and the y-direction.
divergences: Boolean
If True divergences will be plotted in a different color, only if group is either 'prior'
or 'posterior'.
colorbar: bool
If True a colorbar will be included as part of the plot (Defaults to False).
Only works when ``kind=hexbin``
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot.
Read the :ref:`label_guide` for more details and usage examples.
ax: axes, optional
Matplotlib axes or bokeh figures.
divergences_kwargs: dicts, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter` for divergences
scatter_kwargs:
Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter` when using scatter kind
kde_kwargs: dict, optional
Additional keywords passed to :func:`arviz.plot_kde` when using kde kind
hexbin_kwargs: dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.hexbin` when
using hexbin kind
backend: str, optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
backend_kwargs: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`.
marginal_kwargs: dict, optional
Additional keywords passed to :func:`arviz.plot_dist`, modifying the
marginal distributions plotted in the diagonal.
point_estimate: str, optional
Select point estimate from 'mean', 'mode' or 'median'. The point estimate will be
plotted using a scatter marker and vertical/horizontal lines.
point_estimate_kwargs: dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.axvline`,
:meth:`matplotlib.axes.Axes.axhline` (matplotlib) or
:class:`bokeh:bokeh.models.Span` (bokeh)
point_estimate_marker_kwargs: dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter`
or :meth:`bokeh:bokeh.plotting.Figure.square` in point
estimate plot. Not available in bokeh
reference_values: dict, optional
Reference values for the plotted variables. The Reference values will be plotted
using a scatter marker
reference_values_kwargs: dict, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.plot` or
:meth:`bokeh:bokeh.plotting.Figure.circle` in reference values plot
show: bool, optional
Call backend show function.
Returns
-------
axes: matplotlib axes or bokeh figures
Examples
--------
KDE Pair Plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> centered = az.load_arviz_data('centered_eight')
>>> coords = {'school': ['Choate', 'Deerfield']}
>>> az.plot_pair(centered,
>>> var_names=['theta', 'mu', 'tau'],
>>> kind='kde',
>>> coords=coords,
>>> divergences=True,
>>> textsize=18)
Hexbin pair plot
.. plot::
:context: close-figs
>>> az.plot_pair(centered,
>>> var_names=['theta', 'mu'],
>>> coords=coords,
>>> textsize=18,
>>> kind='hexbin')
Pair plot showing divergences and select variables with regular expressions
.. plot::
:context: close-figs
>>> az.plot_pair(centered,
... var_names=['^t', 'mu'],
... filter_vars="regex",
... coords=coords,
... divergences=True,
... textsize=18)
ArviZPythonPlots.plot_parallel — Method
Plot parallel coordinates plot showing posterior points with and without divergences.
Described by https://arxiv.org/abs/1709.01449
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object
refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names: list of variable names
Variables to be plotted, if `None` all variables are plotted. Can be used to change the
order of the plotted variables. 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: mapping, optional
Coordinates of ``var_names`` to be plotted.
Passed to :meth:`xarray.Dataset.sel`.
figsize: tuple
Figure size. If None it will be defined automatically.
textsize: float
Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
on ``figsize``.
legend: bool
Flag for plotting legend (defaults to True)
colornd: valid matplotlib color
color for non-divergent points. Defaults to 'k'
colord: valid matplotlib color
color for divergent points. Defaults to 'C1'
shadend: float
Alpha blending value for non-divergent points, between 0 (invisible) and 1 (opaque).
Defaults to .025
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot.
Read the :ref:`label_guide` for more details and usage examples.
ax: axes, optional
Matplotlib axes or bokeh figures.
norm_method: str
Method for normalizing the data. Methods include normal, minmax and rank.
Defaults to none.
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: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`.
show: bool, optional
Call backend show function.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_pair : Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal.
plot_trace : Plot distribution (histogram or kernel density estimates) and sampled values
or rank plot
Examples
--------
Plot default parallel plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_parallel(data, var_names=["mu", "tau"])
Plot parallel plot with normalization
.. plot::
:context: close-figs
>>> az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="normal")
Plot parallel plot with minmax
.. plot::
:context: close-figs
>>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="minmax")
>>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
Plot parallel plot with rank
.. plot::
:context: close-figs
>>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="rank")
>>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ArviZPythonPlots.plot_posterior — Method
Plot Posterior densities in the style of John K. Kruschke's book.
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object.
Refer to the documentation of :func:`arviz.convert_to_dataset` for details
var_names: list of variable names
Variables to be plotted, two variables are required. Prefix the variables with ``~``
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``.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See the :ref:`this section <common_combine_dims>` for usage examples.
transform: callable
Function to transform data (defaults to None i.e.the identity function)
coords: mapping, optional
Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`
grid : tuple
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.
textsize: float
Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
on ``figsize``.
hdi_prob: float, optional
Plots highest density interval for chosen percentage of density.
Use 'hide' to hide the highest density interval. Defaults to 0.94.
multimodal: bool
If true (default) it may compute more than one credible interval if the distribution is
multimodal and the modes are well separated.
skipna : bool
If true ignores nan values when computing the hdi and point estimates. Defaults to false.
round_to: int, optional
Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger.
point_estimate: Optional[str]
Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None.
Defaults to 'auto' i.e. it falls back to default set in rcParams.
group: str, optional
Specifies which InferenceData group should be plotted. Defaults to 'posterior'.
rope : list, tuple or dictionary of {str: tuples or lists}, optional
A dictionary of tuples with the lower and upper values of the Region Of Practical
Equivalence. See :ref:`this section <common_rope>` for usage examples.
ref_val: float or dictionary of floats
display the percentage below and above the values in ref_val. Must be None (default),
a constant, a list or a dictionary like see an example below. If a list is provided, its
length should match the number of variables.
rope_color: str, optional
Specifies the color of ROPE and displayed percentage within ROPE
ref_val_color: str, optional
Specifies the color of the displayed percentage
kind: str
Type of plot to display (kde or hist) For discrete variables this argument is ignored and
a histogram is always used. Defaults to rcParam ``plot.density_kind``
bw: float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental" when `circular` is False
and "taylor" (for now) when `circular` is True.
Defaults to "default" which means "experimental" when variable is not circular
and "taylor" when it is. Only works if `kind == kde`.
circular: bool, optional
If True, it interprets the values passed are from a circular variable measured in radians
and a circular KDE is used. Only valid for 1D KDE. Defaults to False.
Only works if `kind == kde`.
bins: integer or sequence or 'auto', optional
Controls the number of bins,accepts the same keywords :func:`matplotlib.pyplot.hist` does.
Only works if `kind == hist`. If None (default) it will use `auto` for continuous variables
and `range(xmin, xmax + 1)` for discrete variables.
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`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_kwargs: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`
show: bool, optional
Call backend show function.
**kwargs
Passed as-is to :func:`matplotlib.pyplot.hist` or :func:`matplotlib.pyplot.plot` function
depending on the value of `kind`.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_dist : Plot distribution as histogram or kernel density estimates.
plot_density : Generate KDE plots for continuous variables and histograms for discrete ones.
plot_forest : Forest plot to compare HDI intervals from a number of distributions.
Examples
--------
Show a default kernel density plot following style of John Kruschke
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_posterior(data)
Plot subset variables by specifying variable name exactly
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu'])
Plot Region of Practical Equivalence (rope) and select variables with regular expressions
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu', '^the'], filter_vars="regex", rope=(-1, 1))
Plot Region of Practical Equivalence for selected distributions
.. plot::
:context: close-figs
>>> rope = {'mu': [{'rope': (-2, 2)}], 'theta': [{'school': 'Choate', 'rope': (2, 4)}]}
>>> az.plot_posterior(data, var_names=['mu', 'theta'], rope=rope)
Using `coords` argument to plot only a subset of data
.. plot::
:context: close-figs
>>> coords = {"school": ["Choate","Phillips Exeter"]}
>>> az.plot_posterior(data, var_names=["mu", "theta"], coords=coords)
Add reference lines
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu', 'theta'], ref_val=0)
Show point estimate of distribution
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu', 'theta'], point_estimate='mode')
Show reference values using variable names and coordinates
.. plot::
:context: close-figs
>>> az.plot_posterior(data, ref_val= {"theta": [{"school": "Deerfield", "ref_val": 4},
... {"school": "Choate", "ref_val": 3}]})
Show reference values using a list
.. plot::
:context: close-figs
>>> az.plot_posterior(data, ref_val=[1] + [5] * 8 + [1])
Plot posterior as a histogram
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu'], kind='hist')
Change size of highest density interval
.. plot::
:context: close-figs
>>> az.plot_posterior(data, var_names=['mu'], hdi_prob=.75)
ArviZPythonPlots.plot_ppc — Method
Plot for posterior/prior predictive checks.
Parameters
----------
data : InferenceData
:class:`arviz.InferenceData` object containing the observed and posterior/prior
predictive data.
kind : str, default "kde"
Type of plot to display ("kde", "cumulative", or "scatter").
alpha : float, optional
Opacity of posterior/prior predictive density curves.
Defaults to 0.2 for ``kind = kde`` and cumulative, for scatter defaults to 0.7.
mean : bool, default True
Whether or not to plot the mean posterior/prior predictive distribution.
observed : bool, optional
Whether or not to plot the observed data. Defaults to True for ``group = posterior``
and False for ``group = prior``.
observed_rug : bool, default False
Whether or not to plot a rug plot for the observed data. Only valid if `observed` is
`True` and for kind `kde` or `cumulative`.
color : list, optional
List with valid matplotlib colors corresponding to the posterior/prior predictive
distribution, observed data and mean of the posterior/prior predictive distribution.
Defaults to ["C0", "k", "C1"].
grid : tuple, optional
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``.
data_pairs : dict, optional
Dictionary containing relations between observed data and posterior/prior predictive data.
Dictionary structure:
- key = data var_name
- value = posterior/prior predictive var_name
For example, ``data_pairs = {'y' : 'y_hat'}``
If None, it will assume that the observed data and the posterior/prior
predictive data have the same variable name.
var_names : list of str, optional
Variables to be plotted, if `None` all variable are plotted. Prefix the
variables by ``~`` when you want to exclude them from the plot.
filter_vars : {None, "like", "regex"}, 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
Dictionary mapping dimensions to selected coordinates to be plotted.
Dimensions without a mapping specified will include all coordinates for
that dimension. Defaults to including all coordinates for all
dimensions if None.
flatten : list
List of dimensions to flatten in ``observed_data``. Only flattens across the coordinates
specified in the ``coords`` argument. Defaults to flattening all of the dimensions.
flatten_pp : list
List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens
across the coordinates specified in the ``coords`` argument. Defaults to flattening all
of the dimensions. Dimensions should match flatten excluding dimensions for ``data_pairs``
parameters. If ``flatten`` is defined and ``flatten_pp`` is None, then
``flatten_pp = flatten``.
num_pp_samples : int
The number of posterior/prior predictive samples to plot. For ``kind`` = 'scatter' and
``animation = False`` if defaults to a maximum of 5 samples and will set jitter to 0.7.
unless defined. Otherwise it defaults to all provided samples.
random_seed : int
Random number generator seed passed to ``numpy.random.seed`` to allow
reproducibility of the plot. By default, no seed will be provided
and the plot will change each call if a random sample is specified
by ``num_pp_samples``.
jitter : float, default 0
If ``kind`` is "scatter", jitter will add random uniform noise to the height
of the ppc samples and observed data.
animated : bool, default False
Create an animation of one posterior/prior predictive sample per frame.
Only works with matploblib backend.
To run animations inside a notebook you have to use the `nbAgg` matplotlib's backend.
Try with `%matplotlib notebook` or `%matplotlib nbAgg`. You can switch back to the
default matplotlib's backend with `%matplotlib inline` or `%matplotlib auto`.
If switching back and forth between matplotlib's backend, you may need to run twice the cell
with the animation.
If you experience problems rendering the animation try setting
``animation_kwargs({'blit':False})`` or changing the matplotlib's backend (e.g. to TkAgg)
If you run the animation from a script write ``ax, ani = az.plot_ppc(.)``
animation_kwargs : dict
Keywords passed to :class:`matplotlib.animation.FuncAnimation`. Ignored with
matplotlib backend.
legend : bool, default True
Add legend to figure.
labeller : labeller, optional
Class providing the method ``make_pp_label`` to generate the labels in the plot titles.
Read the :ref:`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 to "matplotlib".
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
group : {"prior", "posterior"}, optional
Specifies which InferenceData group should be plotted. Defaults to 'posterior'.
Other value can be 'prior'.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib_axes or bokeh_figures
ani : matplotlib.animation.FuncAnimation, optional
Only provided if `animated` is ``True``.
See Also
--------
plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive.
plot_loo_pit : Plot for posterior predictive checks using cross validation.
plot_lm : Posterior predictive and mean plots for regression-like data.
plot_ts : Plot timeseries data.
Examples
--------
Plot the observed data KDE overlaid on posterior predictive KDEs.
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('radon')
>>> az.plot_ppc(data, data_pairs={"y":"y"})
Plot the overlay with empirical CDFs.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, kind='cumulative')
Use the ``coords`` and ``flatten`` parameters to plot selected variable dimensions
across multiple plots. We will now modify the dimension ``obs_id`` to contain
indicate the name of the county where the measure was taken. The change has to
be done on both ``posterior_predictive`` and ``observed_data`` groups, which is
why we will use :meth:`~arviz.InferenceData.map` to apply the same function to
both groups. Afterwards, we will select the counties to be plotted with the
``coords`` arg.
.. plot::
:context: close-figs
>>> obs_county = data.posterior["County"][data.constant_data["county_idx"]]
>>> data = data.assign_coords(obs_id=obs_county, groups="observed_vars")
>>> az.plot_ppc(data, coords={'obs_id': ['ANOKA', 'BELTRAMI']}, flatten=[])
Plot the overlay using a stacked scatter plot that is particularly useful
when the sample sizes are small.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, kind='scatter', flatten=[],
>>> coords={'obs_id': ['AITKIN', 'BELTRAMI']})
Plot random posterior predictive sub-samples.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, num_pp_samples=30, random_seed=7)
ArviZPythonPlots.plot_rank — Method
Plot rank order statistics of chains.
From the paper: Rank plots are histograms of the ranked posterior draws (ranked over all
chains) plotted separately for each chain.
If all of the chains are targeting the same posterior, we expect the ranks in each chain to be
uniform, whereas if one chain has a different location or scale parameter, this will be
reflected in the deviation from uniformity. If rank plots of all chains look similar, this
indicates good mixing of the chains.
This plot was introduced by Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter,
Paul-Christian Burkner (2021): Rank-normalization, folding, and localization:
An improved R-hat for assessing convergence of MCMC. Bayesian analysis, 16(2):667-718.
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object.
Refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names: string or list of variable names
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``.
transform: callable
Function to transform data (defaults to None i.e.the identity function)
coords: mapping, optional
Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`
bins: None or passed to np.histogram
Binning strategy used for histogram. By default uses twice the result of Sturges' formula.
See :func:`numpy.histogram` documentation for, other available arguments.
kind: string
If bars (defaults), ranks are represented as stacked histograms (one per chain). If vlines
ranks are represented as vertical lines above or below ``ref_line``.
colors: string or list of strings
List with valid matplotlib colors, one color per model. Alternative a string can be passed.
If the string is `cycle`, it will automatically choose a color per model from matplotlib's
cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all
models. Defaults to `cycle`.
ref_line: boolean
Whether to include a dashed line showing where a uniform distribution would lie
labels: bool
whether to plot or not the x and y labels, defaults to True
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
grid : tuple
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.
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".
ref_line_kwargs : dict, optional
Reference line keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.axhline` or
:class:`bokeh:bokeh.models.Span`.
bar_kwargs : dict, optional
Bars keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.bar` or
:meth:`bokeh:bokeh.plotting.Figure.vbar`.
vlines_kwargs : dict, optional
Vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.vlines` or
:meth:`bokeh:bokeh.plotting.Figure.multi_line`.
marker_vlines_kwargs : dict, optional
Marker for the vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or
:meth:`bokeh:bokeh.plotting.Figure.circle`.
backend_kwargs: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`. For additional documentation
check the plotting method of the backend.
show: bool, optional
Call backend show function.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_trace : Plot distribution (histogram or kernel density estimates) and
sampled values or rank plot.
Examples
--------
Show a default rank plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_rank(data)
Recreate Figure 13 from the arxiv preprint
.. plot::
:context: close-figs
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_rank(data, var_names='tau')
Use vlines to compare results for centered vs noncentered models
.. plot::
:context: close-figs
>>> import matplotlib.pyplot as plt
>>> centered_data = az.load_arviz_data('centered_eight')
>>> noncentered_data = az.load_arviz_data('non_centered_eight')
>>> _, ax = plt.subplots(1, 2, figsize=(12, 3))
>>> az.plot_rank(centered_data, var_names="mu", kind='vlines', ax=ax[0])
>>> az.plot_rank(noncentered_data, var_names="mu", kind='vlines', ax=ax[1])
Change the aesthetics using kwargs
.. plot::
:context: close-figs
>>> az.plot_rank(noncentered_data, var_names="mu", kind="vlines",
>>> vlines_kwargs={'lw':0}, marker_vlines_kwargs={'lw':3});
ArviZPythonPlots.plot_separation — Method
Separation plot for binary outcome models.
Model predictions are sorted and plotted using a color code according to
the observed data.
Parameters
----------
idata : InferenceData
:class:`arviz.InferenceData` object.
y : array, DataArray or str
Observed data. If str, ``idata`` must be present and contain the observed data group
y_hat : array, DataArray or str
Posterior predictive samples for ``y``. It must have the same shape as ``y``. If str or
None, ``idata`` must contain the posterior predictive group.
y_hat_line : bool, optional
Plot the sorted ``y_hat`` predictions.
expected_events : bool, optional
Plot the total number of expected events.
figsize : figure 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``.
color : str, optional
Color to assign to the positive class. The negative class will be plotted using the
same color and an `alpha=0.3` transparency.
legend : bool, optional
Show the legend of the figure.
ax: axes, optional
Matplotlib axes or bokeh figures.
plot_kwargs : dict, optional
Additional keywords passed to :meth:`mpl:matplotlib.axes.Axes.bar` or
:meth:`bokeh:bokeh.plotting.Figure.vbar` for separation plot.
y_hat_line_kwargs : dict, optional
Additional keywords passed to ax.plot for ``y_hat`` line.
exp_events_kwargs : dict, optional
Additional keywords passed to ax.scatter for ``expected_events`` marker.
backend: str, optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
backend_kwargs: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`.
show : bool, optional
Call backend show function.
Returns
-------
axes : matplotlib axes or bokeh figures
See Also
--------
plot_ppc : Plot for posterior/prior predictive checks.
References
----------
.. [1] Greenhill, B. *et al.*, The Separation Plot: A New Visual Method
for Evaluating the Fit of Binary Models, *American Journal of
Political Science*, (2011) see https://doi.org/10.1111/j.1540-5907.2011.00525.x
Examples
--------
Separation plot for a logistic regression model.
.. plot::
:context: close-figs
>>> import arviz as az
>>> idata = az.load_arviz_data('classification10d')
>>> az.plot_separation(idata=idata, y='outcome', y_hat='outcome', figsize=(8, 1))
ArviZPythonPlots.plot_trace — Method
Plot distribution (histogram or kernel density estimates) and sampled values or rank plot.
If `divergences` data is available in `sample_stats`, will plot the location of divergences as
dashed vertical lines.
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names: str or list of str, optional
One or more 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 of {str: slice or array_like}, optional
Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`
divergences: {"bottom", "top", None}, optional
Plot location of divergences on the traceplots.
kind: {"trace", "rank_bars", "rank_vlines"}, optional
Choose between plotting sampled values per iteration and rank plots.
transform: callable, optional
Function to transform data (defaults to None i.e.the identity function)
figsize: tuple of (float, float), optional
If None, size is (12, variables * 2)
rug: bool, optional
If True adds a rugplot of samples. Defaults to False. Ignored for 2D KDE.
Only affects continuous variables.
lines: list of tuple of (str, dict, array_like), optional
List of (var_name, {'coord': selection}, [line, positions]) to be overplotted as
vertical lines on the density and horizontal lines on the trace.
circ_var_names : str or list of str, optional
List of circular variables to account for when plotting KDE.
circ_var_units : str
Whether the variables in ``circ_var_names`` are in "degrees" or "radians".
compact: bool, optional
Plot multidimensional variables in a single plot.
compact_prop: str or dict {str: array_like}, optional
Defines the property name and the property values to distinguish different
dimensions with compact=True.
When compact=True it defaults to color, it is
ignored otherwise.
combined: bool, optional
Flag for combining multiple chains into a single line. If False (default), chains will be
plotted separately.
chain_prop: str or dict {str: array_like}, optional
Defines the property name and the property values to distinguish different chains.
If compact=True it defaults to linestyle,
otherwise it uses the color to distinguish
different chains.
legend: bool, optional
Add a legend to the figure with the chain color code.
plot_kwargs, fill_kwargs, rug_kwargs, hist_kwargs: dict, optional
Extra keyword arguments passed to :func:`arviz.plot_dist`. Only affects continuous
variables.
trace_kwargs: dict, optional
Extra keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
rank_kwargs : dict, optional
Extra keyword arguments passed to :func:`arviz.plot_rank`
axes: axes, optional
Matplotlib axes or bokeh figures.
backend: {"matplotlib", "bokeh"}, optional
Select plotting backend.
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
:func:`matplotlib.pyplot.subplots` or
:func:`bokeh.plotting.figure`.
show: bool, optional
Call backend show function.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_rank : Plot rank order statistics of chains.
Examples
--------
Plot a subset variables and select them with partial naming
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('non_centered_eight')
>>> coords = {'school': ['Choate', 'Lawrenceville']}
>>> az.plot_trace(data, var_names=('theta'), filter_vars="like", coords=coords)
Show all dimensions of multidimensional variables in the same plot
.. plot::
:context: close-figs
>>> az.plot_trace(data, compact=True)
Display a rank plot instead of trace
.. plot::
:context: close-figs
>>> az.plot_trace(data, var_names=["mu", "tau"], kind="rank_bars")
Combine all chains into one distribution and select variables with regular expressions
.. plot::
:context: close-figs
>>> az.plot_trace(
>>> data, var_names=('^theta'), filter_vars="regex", coords=coords, combined=True
>>> )
Plot reference lines against distribution and trace
.. plot::
:context: close-figs
>>> lines = (('theta_t',{'school': "Choate"}, [-1]),)
>>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, lines=lines)
ArviZPythonPlots.plot_violin — Method
Plot posterior of traces as violin plot.
Notes
-----
If multiple chains are provided for a variable they will be combined
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names: list of variable names, optional
Variables to be plotted, if None all variable are plotted. Prefix the
variables by ``~`` when you want to exclude them from the plot.
combine_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See the :ref:`this section <common_combine_dims>` for usage examples.
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``.
transform: callable
Function to transform data (defaults to None i.e. the identity function).
quartiles: bool, optional
Flag for plotting the interquartile range, in addition to the ``hdi_prob`` * 100%
intervals. Defaults to ``True``.
rug: bool
If ``True`` adds a jittered rugplot. Defaults to ``False``.
side : {"both", "left", "right"}, default "both"
If ``both``, both sides of the violin plot are rendered. If ``left`` or ``right``, only
the respective side is rendered. By separately plotting left and right halfs with
different data, split violin plots can be achieved.
hdi_prob: float, optional
Plots highest posterior density interval for chosen percentage of density.
Defaults to 0.94.
shade: float
Alpha blending value for the shaded area under the curve, between 0
(no shade) and 1 (opaque). Defaults to 0.
bw: float or str, optional
If numeric, indicates the bandwidth and must be positive.
If str, indicates the method to estimate the bandwidth and must be
one of "scott", "silverman", "isj" or "experimental" when ``circular`` is ``False``
and "taylor" (for now) when ``circular`` is ``True``.
Defaults to "default" which means "experimental" when variable is not circular
and "taylor" when it is.
circular: bool, optional.
If ``True``, it interprets `values` is a circular variable measured in radians
and a circular KDE is used. Defaults to ``False``.
grid : tuple
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.
textsize: int
Text size of the point_estimates, axis ticks, and highest density interval. If None it will
be autoscaled based on ``figsize``.
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
sharex: bool
Defaults to ``True``, violinplots share a common x-axis scale.
sharey: bool
Defaults to ``True``, violinplots share a common y-axis scale.
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).
shade_kwargs: dicts, optional
Additional keywords passed to :meth:`matplotlib.axes.Axes.fill_between`, or
:meth:`matplotlib.axes.Axes.barh` to control the shade.
rug_kwargs: dict
Keywords passed to the rug plot. If true only the right half side of the violin will be
plotted.
backend: str, optional
Select plotting backend {"matplotlib","bokeh"}. Default to "matplotlib".
backend_kwargs: bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show: bool, optional
Call backend show function.
Returns
-------
axes: matplotlib axes or bokeh figures
See Also
--------
plot_forest: Forest plot to compare HDI intervals from a number of distributions.
Examples
--------
Show a default violin plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_violin(data)