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

Plot fit against one regressor.

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

  • results (result instance) – result instance with resid, model.endog and model.exog as attributes
  • x_var (int or 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 (Matplotlib AxesSubplot instance, optional) – If given, this subplot is used to plot in instead of a new figure being created.
  • kwargs – The keyword arguments are passed to the plot command for the fitted values points.

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

Return type:

Matplotlib figure instance


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’:

>>> 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)


© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.