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: |
|
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Returns: |
fig – If |
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.
http://www.statsmodels.org/stable/generated/statsmodels.graphics.regressionplots.plot_fit.html