arviz_plots.plot_forest

arviz_plots.plot_forest(dt, var_names=None, filter_vars=None, group='posterior', coords=None, sample_dims=None, combined=False, point_estimate=None, ci_kind=None, ci_probs=None, labels=None, shade_label=None, plot_collection=None, backend=None, labeller=None, aes_map=None, plot_kwargs=None, stats_kwargs=None, pc_kwargs=None)

Plot 1D marginal credible intervals in a single plot.

Parameters:

dtdatatree.DataTree or dict of {strdatatree.DataTree}

Input data. In case of dictionary input, the keys are taken to be model names. In such cases, a dimension “model” is generated and can be used to map to aesthetics.

plot_forest uses the dimension “column” (creating it if necessary) to generate the grid then adds the intervals+point estimates to its “forest” coordinate and labels to its “labels” coordinates. The data used to plot is then the subset column="forest".

var_namesstr 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”}, default None

If None, interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names.

groupstr, default “posterior”

Group to be plotted.

coordsdict, optional

sample_dimsstr or sequence of hashable, optional

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

combinedbool, default False

Whether to plot intervals for each chain or not. Ignored when the “chain” dimension is not present.

point_estimate{“mean”, “median”, “mode”}, optional

Which point estimate to plot. Defaults to rcParams["plot.point_estimate"]

ci_kind{“eti”, “hdi”}, optional

Which credible interval to use. Defaults to rcParams["stats.ci_kind"]

ci_probs(float, float), optional

Indicates the probabilities that should be contained within the plotted credible intervals. It should be sorted as the elements refer to the probabilities of the “trunk” and “twig” elements. Defaults to (0.5, rcParams["stats.ci_prob"])

labelssequence of str, optional

Sequence with the dimensions to be labelled in the plot. By default all dimensions except “chain” and “model” (if present). The order of labels is ignored, only elements being present in it matters. It can include the special “__variable__” indicator, and does so by default.

shade_labelstr, default None

Element of labels that should be used to add shading horizontal strips to the plot. Note that labels and credible intervals are plotted in different plots. The shading is applied to both plots, and the spacing between them is set to 0 if possible, which is not always the case (one notable example being matplotlib’s constrained layout).

plot_collectionPlotCollection, optional

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

labellerlabeller, optional

aes_mapmapping of {strsequence of str or False}, optional

Mapping of artists to aesthetics that should use their mapping in plot_collection when plotted. Valid keys are the same as for plot_kwargs except “ticklabels” which doesn’t apply and “twig” and “trunk” which, similarly to stats_kwargs take the same aesthetics through the “credible_interval” key.

By default, aesthetic mappings are generated for: y, alpha, overlay and color (if multiple models are present). All aesthetic mappings but alpha are applied to both the credible intervals and the point estimate; overlay is applied to labels; and both overlay and alpha are applied to the shade.

“overlay” is a dummy aesthetic to trigger looping over variables and/or dimensions using all aesthetics in every iteration. “alpha” gets two values (0, 0.3) in order to trigger the alternate shading effect.

plot_kwargsmapping of {strmapping or False}, optional

Valid keys are:

• trunk, twig -> passed to line_x

• point_estimate -> passed to scatter_x

• labels -> passed to annotate_label

• shade -> passed to fill_between_y

• ticklabels -> passed to xticks

• remove_axis -> not passed anywhere, can only take False as value to skip calling remove_axis

stats_kwargsmapping, optional

Valid keys are:

• credible_interval -> passed to eti or hdi

• point_estimate -> passed to mean, median or mode

pc_kwargsmapping

Passed to arviz_plots.PlotCollection.grid

Returns:

PlotCollection

Notes

The separation between variables and all its coordinate values is set to 1. The only two exceptions to this are the dimensions named “chain” and “model” in case they are present, which get a smaller spacing to give a sense of grouping among visual elements that only differ on their chain or model id.

Examples

The following examples focus on behaviour specific to plot_forest. For a general introduction to batteries-included functions like this one and common usage examples see Introduction to batteries-included plots in arviz_plots

Default forest plot for a single model:

>>> from arviz_plots import plot_forest, style
>>> style.use("arviz-clean")
>>> from arviz_base import load_arviz_data
>>> centered = load_arviz_data('centered_eight')
>>> non_centered = load_arviz_data('non_centered_eight')
>>> pc = plot_forest(centered)

Default forest plot for multiple models:

>>> pc = plot_forest({"centered": centered, "non centered": non_centered})
>>> pc.add_legend("model")

Single model forest plot with color mapped to the variable (mapping which is also applied to the labels) and alternate shading per school. Moreover, to ensure the shading looks continuous, we’ll specify we don’t want to use constrained layout (set by the “arviz-clean” theme) and to avoid having the labels too squished we’ll set the width_ratios for create_plotting_grid via pc_kwargs.

>>> pc = plot_forest(
>>>     non_centered,
>>>     var_names=["theta", "mu", "theta_t", "tau"],
>>>     pc_kwargs={
>>>         "aes": {"color": ["__variable__"]},
>>>         "plot_grid_kws": {"width_ratios": [1, 2], "layout": "none"}
>>>     },
>>>     aes_map={"labels": ["color"]},
>>>     shade_label="school",
>>> )

Extend the forest plot with an extra plot with ess estimates. To achieve that, we manually add a “column” dimension with size 3. plot_forest only plots on the “labels” and “forest” coordinate values, leaving the “ess” coordinate empty. Afterwards, we manually use PlotCollection.map with the ess result as data on the “ess” column to plot their values.

>>> from arviz_plots import visuals
>>> from arviz_stats.base import ess
>>>
>>> c_aux = centered["posterior"].expand_dims(
>>>     column=3
>>> ).assign_coords(column=["labels", "forest", "ess"])
>>> pc = plot_forest(c_aux, combined=True)
>>> pc.map(
>>>     visuals.scatter_x, "ess", data=ess(centered),
>>>     coords={"column": "ess"}, color="C0"
>>> )

Note that we are using the same PlotCollection, so when using map all the same aesthetic mappings used by plot_forest are used.