statsmodels.graphics.regressionplots.plot_fit

statsmodels.graphics.regressionplots.plot_fit(results, exog_idx, y_true=None, ax=None, vlines=True, **kwargs)[source]

Plot fit against one regressor.

This creates one graph with the scatterplot of observed values compared to fitted values.

Parameters:

  • results (Results) – A result instance with resid, model.endog and model.exog as attributes.

  • exog_idx ({int, str}) – Name or index of regressor in exog matrix.

  • y_true (array_like. optional) – If this is not None, then the array is added to the plot.

  • ax (AxesSubplot, optional) – If given, this subplot is used to plot in instead of a new figure being created.

  • vlines (bool, optional) – If this not True, then the uncertainty (pointwise prediction intervals) of the fit is not plotted.

  • **kwargs – The keyword arguments are passed to the plot command for the fitted values points.

Returns:

If ax is None, the created figure. Otherwise the figure to which ax is connected.

Return type:

Figure

Examples

Load the Statewide Crime data set and perform linear regression with poverty and hs_grad as variables and murder as the response

>>> import statsmodels.api as sm
>>> import matplotlib.pyplot as plt

>>> data = sm.datasets.statecrime.load_pandas().data
>>> murder = data['murder']
>>> X = data[['poverty', 'hs_grad']]

>>> X["constant"] = 1
>>> y = murder
>>> model = sm.OLS(y, X)
>>> results = model.fit()

Create a plot just for the variable ‘Poverty.’ Note that vertical bars representing uncertainty are plotted since vlines is true

>>> fig, ax = plt.subplots()
>>> fig = sm.graphics.plot_fit(results, 0, ax=ax)
>>> ax.set_ylabel("Murder Rate")
>>> ax.set_xlabel("Poverty Level")
>>> ax.set_title("Linear Regression")

>>> plt.show()

(Source code, png, hires.png, pdf)

../_images/graphics_plot_fit_ex.png