voiage.plot.ceac.plot_ceac

plot_ceac

plot_ceac([positional or keyword] value_array: ValueArray = None, [positional or keyword] wtp_thresholds: np.ndarray | list[float] = None, [positional or keyword] strategy_names: list[str] | None = None, [positional or keyword] xlabel: str = 'Willingness-to-Pay Threshold', [positional or keyword] ylabel: str = 'Probability Cost-Effective', [positional or keyword] title: str = 'Cost-Effectiveness Acceptability Curve (CEAC)', [positional or keyword] ax: Axes | None = None, [variadic keyword] plot_kwargs_per_strategy: object = {}) -> Axes

Plot a cost-effectiveness acceptability curve.

Parameters

value_array : ValueArray 3D net-benefit surface with samples, strategies, and WTP thresholds. wtp_thresholds : numpy.ndarray or list[float] Willingness-to-pay thresholds used for the x-axis. strategy_names : list[str], optional Override strategy names. xlabel : str, default=“Willingness-to-Pay Threshold” X-axis label. ylabel : str, default=“Probability Cost-Effective” Y-axis label. title : str, default=“Cost-Effectiveness Acceptability Curve (CEAC)” Plot title. ax : matplotlib.axes.Axes, optional Axes to draw on. **plot_kwargs_per_strategy : object Optional list of per-strategy keyword argument dictionaries.

Returns

matplotlib.axes.Axes Axes containing the CEAC plot.

Notes

A CEAC shows the probability that each strategy is optimal across the supplied willingness-to-pay thresholds.

Parameters:

• value_array ValueArray
• wtp_thresholds np.ndarray | list[float]
• strategy_names list[str] | None (default: None)
• xlabel str (default: 'Willingness-to-Pay Threshold')
• ylabel str (default: 'Probability Cost-Effective')
• title str (default: 'Cost-Effectiveness Acceptability Curve (CEAC)')
• ax Axes | None (default: None)
• plot_kwargs_per_strategy object (default: {})

Returns: Axes