graphics.regressionplots.influence_plot()

statsmodels.graphics.regressionplots.influence_plot

statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source]

Plot of influence in regression. Plots studentized resids vs. leverage.

Parameters:

results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha value to identify large studentized residuals. Large means abs(resid_studentized) > t.ppf(1-alpha/2, dof=results.df_resid) criterion : str {?DFFITS?, ?Cooks?} Which criterion to base the size of the points on. Options are DFFITS or Cook?s D. size : float The range of criterion is mapped to 10**2 - size**2 in points. plot_alpha : float The alpha of the plotted points. ax : matplotlib Axes instance An instance of a matplotlib Axes.

Returns:

fig : matplotlib figure The matplotlib figure that contains the Axes.

Notes

Row labels for the observations in which the leverage, measured by the diagonal of the hat matrix, is high or the residuals are large, as the combination of large residuals and a high influence value indicates an influence point. The value of large residuals can be controlled using the alpha parameter. Large leverage points are identified as hat_i > 2 * (df_model + 1)/nobs.