drippy.regression
=================

.. py:module:: drippy.regression

.. autoapi-nested-parse::

   Plotting functions for regression models (y = f(x) + e, x continuous).



Functions
---------

.. autoapisummary::

   drippy.regression.scatter_plot
   drippy.regression.six_plot
   drippy.regression.linear_correlation_plot
   drippy.regression.linear_intercept_plot
   drippy.regression.linear_slope_plot
   drippy.regression.linear_residual_sd_plot


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

.. py:function:: scatter_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

   Creates a scatter plot of y vs x.

   Also used in regression context (see drippy.regression).

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param ax: Matplotlib axes. If None, creates new axes.

   :returns: The figure and axes containing the plot.


.. py:function:: six_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, axes: numpy.ndarray | None = None) -> tuple[matplotlib.figure.Figure, numpy.ndarray]

   Creates a 2x3 composite regression diagnostic plot.

   The six panels are: scatter with regression line, residuals vs x, lag
   plot of residuals, histogram of residuals, normal probability plot of
   residuals, and run sequence of residuals.

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param axes: 2x3 array of Axes. If None, creates new axes.

   :returns: (fig, axes) where axes has shape (2, 3).


.. py:function:: linear_correlation_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None, window: int = 10) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

   Plots Pearson correlation coefficient for rolling windows of data.

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param ax: Matplotlib axes. If None, creates new axes.
   :param window: Number of observations per rolling window.

   :returns: The figure and axes containing the plot.


.. py:function:: linear_intercept_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None, window: int = 10) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

   Plots OLS regression intercept for rolling windows of data.

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param ax: Matplotlib axes. If None, creates new axes.
   :param window: Number of observations per rolling window.

   :returns: The figure and axes containing the plot.


.. py:function:: linear_slope_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None, window: int = 10) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

   Plots OLS regression slope for rolling windows of data.

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param ax: Matplotlib axes. If None, creates new axes.
   :param window: Number of observations per rolling window.

   :returns: The figure and axes containing the plot.


.. py:function:: linear_residual_sd_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None, window: int = 10) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

   Plots residual standard deviation for rolling windows of data.

   :param data: EDAData container. Requires x.
   :param fig: Matplotlib figure. If None, creates new figure.
   :param ax: Matplotlib axes. If None, creates new axes.
   :param window: Number of observations per rolling window.

   :returns: The figure and axes containing the plot.