ahat.plot_linear_signal_model

plot_linear_signal_model(
    X,
    y,
    X_eval,
    model,
    threshold,
    tau,
    xlog=False,
    ylog=False,
    poi_name='Parameter of Interest',
    ax=None,
)

Diagnostic Plot: Visualizes the standard linear a-hat vs a model.

Plots the raw data, the linear expectation model, the constant 95% prediction interval (calculated using tau), and the target threshold. It automatically scales the plot axes based on the chosen log transformations.

Parameters

Name	Type	Description	Default
X	np.ndarray	Original simulation inputs.	required
y	np.ndarray	Original simulation outcomes.	required
X_eval	np.ndarray	Grid used for curve evaluation.	required
model	LinearRegression	The fitted linear expectation model.	required
threshold	float	The detection threshold limit.	required
tau	float	The constant standard deviation of the residuals.	required
xlog	bool	Set to True if the model used log-transformed X.	False
ylog	bool	Set to True if the model used log-transformed y.	False
poi_name	str	Label for the x-axis. Defaults to “Parameter of Interest”.	'Parameter of Interest'
ax	Optional[plt.Axes]	Existing Matplotlib axes. Created if None.	None

Returns

Name	Type	Description
	plt.Axes	plt.Axes: The configured Matplotlib axis containing the plot.

Examples

import matplotlib.pyplot as plt

ax = plot_linear_signal_model(
    X, y, X_eval, model, threshold=3.5, tau=tau,
    xlog=True, ylog=False, poi_name="Crack Length (mm)"
)
plt.show()