Posterior predictive forest and observations

Posterior predictive forest and observations#

Overlay of forest plot for the posterior predictive samples and the actual observations

Matplotlib version of plot_forest_pp_obs

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from arviz_base import load_arviz_data

import arviz_plots as azp

azp.style.use("arviz-variat")

idata = load_arviz_data("non_centered_eight")
pc = azp.plot_forest(
    idata,
    group="posterior_predictive",
    combined=True,
    labels=["obs_dim_0"],
    backend="none",  # change to preferred backend
)

pc.map(
    azp.visuals.scatter_x,
    "observations",
    data=idata.observed_data.ds,
    coords={"column": "forest"},
    color="gray",
)

pc.map(
    azp.visuals.labelled_x,
    "xlabel",
    coords={"column": "forest"},
    text="Observations",
    ignore_aes="y",
)
pc.show()

See also

API Documentation: plot_forest

Other examples with plot_forest#

Default forest plot with marginal distribution summaries

Forest plot

Forest plot

Forest plot marginal summaries with row shading to enhance reading

Forest plot with shading

Forest plot with shading

Forest plot summaries for 1D marginal distributions

Posterior forest for two models

Posterior forest for two models

Multiple panel visualization with a forest plot and ESS information

Forest plot with ESS

Forest plot with ESS

Draw reference bands to highlight specific regions.

Add Reference Bands

Add Reference Bands

Download Python Source Code: plot_forest_pp_obs.py

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