linreg_ally.plotting
====================

.. py:module:: linreg_ally.plotting


Functions
---------

.. autoapisummary::

   linreg_ally.plotting.qq_and_residuals_plot


Module Contents
---------------

.. py:function:: qq_and_residuals_plot(y_actual, y_predicted, concatenate=True)

   Generate a Q-Q plot (standardized residuals vs theoretical quantiles)
   and a Residuals vs. Fitted Values plot using Altair for regression diagnostics.

   :param y_actual: Actual observed values from the dataset. Must be numeric and have the same length as `y_predicted`.
   :type y_actual: array-like
   :param y_predicted: Predicted (fitted) values from the regression model. Must be numeric and have the same length as `y_actual`.
   :type y_predicted: array-like
   :param concatenate: If True, concatenates the Q-Q plot and Residuals vs. Fitted Values plot side by side.
                       If False, returns the plots separately as individual Altair charts.
   :type concatenate: bool, optional (default=True)

   :returns:

             - If `concatenate=True`, returns a concatenated Altair chart with both plots.
             - If `concatenate=False`, returns a tuple containing the Q-Q plot and Residuals vs. Fitted Values plot.
   :rtype: alt.Chart or tuple of alt.Chart

   :raises ValueError: If `y_actual` or `y_predicted` are empty, have mismatched lengths, or contain fewer than two data points.
   :raises TypeError: If `y_actual` or `y_predicted` are not numeric.

   .. rubric:: Examples

   >>> import numpy as np
   >>> np.random.seed(42)
   >>> y_actual = np.random.normal(10, 2, 100)
   >>> y_predicted = y_actual + np.random.normal(0, 1, 100)
   >>> chart = qq_and_residuals_plot(y_actual, y_predicted, concatenate=True)
   >>> chart.display()