plot_ce_plane

report.plot_ce_plane(
    outcomes,
    *,
    comparator=None,
    wtp=None,
    effect=None,
    kind='density',
    ax=None,
)

Incremental cost vs incremental effect per iteration on the plane.

With kind="density" (the default) each intervention’s cloud of draws is drawn as nested highest-density regions (the 50%, 80% and 95% regions), which read the spread more clearly than an overplotted scatter once there are many iterations. An intervention with too few iterations to estimate a density falls back to a scatter. kind="scatter" draws every iteration as a point.

Parameters

Name	Type	Description	Default
outcomes	Outcomes	Outcomes from a probabilistic sensitivity analysis.	required
comparator	str | None	Reference intervention (default: outcomes.comparator, or the first intervention if none was flagged).	None
wtp	float | None	If given, draw the willingness-to-pay threshold line.	None
effect	str | None	Effect column (default: primary).	None
kind	str	"density" for highest-density regions, "scatter" for a point per iteration.	'density'
ax	Axes | None	Existing axes to draw on.	None

Example

import pandas as pd from heormodel.models import Outcomes from heormodel.report import plot_ce_plane c = pd.DataFrame({“A”: [0.0, 1.0], “B”: [5.0, 6.0]}) e = pd.DataFrame({“A”: [0.0, 0.1], “B”: [0.5, 0.4]}) ax = plot_ce_plane(Outcomes.from_wide(c, e)) ax.get_xlabel() ‘Incremental effect (qaly)’