drippy.regression

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

Functions

scatter_plot(→ tuple[matplotlib.figure.Figure, ...)	Creates a scatter plot of y vs x.
six_plot(→ tuple[matplotlib.figure.Figure, numpy.ndarray])	Creates a 2x3 composite regression diagnostic plot.
linear_correlation_plot(...)	Plots Pearson correlation coefficient for rolling windows of data.
linear_intercept_plot(...)	Plots OLS regression intercept for rolling windows of data.
linear_slope_plot(→ tuple[matplotlib.figure.Figure, ...)	Plots OLS regression slope for rolling windows of data.
linear_residual_sd_plot(...)	Plots residual standard deviation for rolling windows of data.

Module Contents

drippy.regression.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).

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• ax – Matplotlib axes. If None, creates new axes.

Returns:

The figure and axes containing the plot.

drippy.regression.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.

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• axes – 2x3 array of Axes. If None, creates new axes.

Returns:

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

drippy.regression.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.

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• ax – Matplotlib axes. If None, creates new axes.

• window – Number of observations per rolling window.

Returns:

The figure and axes containing the plot.

drippy.regression.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.

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• ax – Matplotlib axes. If None, creates new axes.

• window – Number of observations per rolling window.

Returns:

The figure and axes containing the plot.

drippy.regression.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.

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• ax – Matplotlib axes. If None, creates new axes.

• window – Number of observations per rolling window.

Returns:

The figure and axes containing the plot.

drippy.regression.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.

Parameters:

• data – EDAData container. Requires x.

• fig – Matplotlib figure. If None, creates new figure.

• ax – Matplotlib axes. If None, creates new axes.

• window – Number of observations per rolling window.

Returns:

The figure and axes containing the plot.