PIT-ECDF

PIT-ECDF#

Faceted plot with PIT Δ-ECDF values for each variable

The plot_ecdf_pit function assumes the values passed to it has already been transformed to PIT values, as in the case of SBC analysis or values from arviz_base.loo_pit.

The distribution should be uniform if the model is well-calibrated.

To make the plot easier to interpret, we plot the Δ-ECDF, that is, the difference between the expected CDF from the observed ECDF. As small deviations from uniformity are expected, the plot also shows the credible envelope.

Matplotlib version of plot_ecdf_pit

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from arviz_base import load_arviz_data

import arviz_plots as azp

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

data = load_arviz_data("sbc")
pc = azp.plot_ecdf_pit(
    data,
    backend="none"  # change to preferred backend
)
pc.show()

See also

API Documentation: plot_ecdf_pit

Other examples with plot_ecdf_pit#

Coverage refers to the proportion of true values that fall within a given prediction interval. For a well-calibrated model, the coverage should match the intended interval width. For example, a 95% credible interval should contain the true value 95% of the time. The distribution should be uniform if the model is well-calibrated. To make the plot easier to interpret, we plot the Δ-ECDF, that is, the difference between the expected CDF from the observed ECDF. As small deviations from uniformity are expected, the plot also shows the credible envelope. We can compute the coverage for equal-tailed intervals (ETI) by passing coverage=True to the plot_ecdf_pit function. This works because ETI coverage can be obtained by transforming the PIT values. However, for other interval types, such as HDI, coverage must be computed explicitly and is not supported by this function.

Coverage ECDF

Coverage ECDF

Download Python Source Code: plot_ecdf_pit.py

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