statsmodels.graphics.regressionplots.plot_fit
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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 : Matplotlib figure instance
If
ax
is None, the created figure. Otherwise the figure to whichax
is connected.Examples
Load the Statewide Crime data set and perform linear regression with
poverty
andhs_grad
as variables andmurder
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)
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