statsmodels.graphics.regressionplots.plot_fit

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.

Parameters:	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.
Returns:	fig – If ax is None, the created figure. Otherwise the figure to which ax is connected.
Return type:	Matplotlib figure instance

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’:

>>> 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()

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