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arviz.plot_forest — ArviZ dev documentation

arviz.plot_forest(data, kind='forestplot', model_names=None, var_names=None, filter_vars=None, transform=None, coords=None, combined=False, combine_dims=None, hdi_prob=None, rope=None, quartiles=True, ess=False, r_hat=False, colors='cycle', textsize=None, linewidth=None, markersize=None, legend=True, labeller=None, ridgeplot_alpha=None, ridgeplot_overlap=2, ridgeplot_kind='auto', ridgeplot_truncate=True, ridgeplot_quantiles=None, figsize=None, ax=None, backend=None, backend_config=None, backend_kwargs=None, show=None)[source]#

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:

dataInferenceData

Any object that can be converted to an arviz.InferenceData object Refer to documentation of 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 \(100\times(hdi\_prob)\%\) 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_nameslist of str, optional

List with names for the models in the list of data. Useful when plotting more that one dataset.

var_nameslist of str, optional

Variables to be plotted. Prefix the variables by ~ when you want to exclude them from the plot. See this section for usage examples.

combine_dimsset_like of str, optional

List of dimensions to reduce. Defaults to reducing only the “chain” and “draw” dimensions. See this section 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 this section for usage examples.

transformcallable(), optional

Function to transform data (defaults to None i.e.the identity function).

coordsdict, optional

Coordinates of var_names to be plotted. Passed to xarray.Dataset.sel(). See this section for usage examples.

combinedbool, default False

Flag for combining multiple chains into a single chain. If False, chains will be plotted separately. See this section for usage examples.

hdi_probfloat, default 0.94

Plots highest posterior density interval for chosen percentage of density. See this section for usage examples.

ropelist, tuple or dictionary of {strtuples or lists}, optional

A dictionary of tuples with the lower and upper values of the Region Of Practical Equivalence. See this section for usage examples.

quartilesbool, default True

Flag for plotting the interquartile range, in addition to the hdi_prob intervals.

r_hatbool, default False

Flag for plotting Split R-hat statistics. Requires 2 or more chains.

essbool, default False

Flag for plotting the effective sample size.

colorslist or 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 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’.

textsizefloat, optional

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

linewidthint, optional

Line width throughout. If None it will be autoscaled based on figsize.

markersizeint, optional

Markersize throughout. If None it will be autoscaled based on figsize.

legendbool, optional

Show a legend with the color encoded model information. Defaults to True, if there are multiple models.

labellerLabeller, optional

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

ridgeplot_alpha: float, optional

Transparency for ridgeplot fill. If ridgeplot_alpha=0, border is colored by model, otherwise a black outline is used.

ridgeplot_overlapfloat, default 2

Overlap height for ridgeplots.

ridgeplot_kindstr, 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_truncatebool, default True

Whether to truncate densities according to the value of hdi_prob.

ridgeplot_quantileslist, 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.

axaxes, optional

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

backend{“matplotlib”, “bokeh”}, default “matplotlib”

Select plotting backend.

backend_configdict, optional

Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams.

backend_kwargsdict, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure. For additional documentation check the plotting method of the backend.

showbool, optional

Call backend show function.

Returns:

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

>>> 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

>>> 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

>>> 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

>>> 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')

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