statsmodels.graphics.regressionplots.plot_leverage_resid2

statsmodels.graphics.regressionplots.plot_leverage_resid2(results, alpha=0.05, ax=None, **kwargs)[source]

Plot leverage statistics vs. normalized residuals squared

Parameters:

  • results (results instance) – A regression results instance

  • alpha (float) – Specifies the cut-off for large-standardized residuals. Residuals are assumed to be distributed N(0, 1) with alpha=alpha.

  • ax (Axes) – Matplotlib Axes instance

  • **kwargs – Additional parameters passed the plot command.

Returns:

A matplotlib figure instance.

Return type:

Figure

Examples

Using a model built from the the state crime dataset, plot the leverage statistics vs. normalized residuals squared. Observations with Large-standardized Residuals will be labeled in the plot.

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

>>> crime_data = sm.datasets.statecrime.load_pandas()
>>> results = smf.ols('murder ~ hs_grad + urban + poverty + single',
...                   data=crime_data.data).fit()
>>> sm.graphics.plot_leverage_resid2(results)
>>> plt.show()

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

../_images/graphics_regression_leverage_resid2.png