statsmodels.graphics.regressionplots.plot_partregress(endog, exog_i, exog_others, data=None, title_kwargs={}, obs_labels=True, label_kwargs={}, ax=None, ret_coords=False, **kwargs)[source]

Plot partial regression for a single regressor.


endog : ndarray or string

endogenous or response variable. If string is given, you can use a arbitrary translations as with a formula.

exog_i : ndarray or string

exogenous, explanatory variable. If string is given, you can use a arbitrary translations as with a formula.

exog_others : ndarray or list of strings

other exogenous, explanatory variables. If a list of strings is given, each item is a term in formula. You can use a arbitrary translations as with a formula. The effect of these variables will be removed by OLS regression.

data : DataFrame, dict, or recarray

Some kind of data structure with names if the other variables are given as strings.

title_kwargs : dict

Keyword arguments to pass on for the title. The key to control the fonts is fontdict.

obs_labels : bool or array-like

Whether or not to annotate the plot points with their observation labels. If obs_labels is a boolean, the point labels will try to do the right thing. First it will try to use the index of data, then fall back to the index of exog_i. Alternatively, you may give an array-like object corresponding to the obseveration numbers.

labels_kwargs : dict

Keyword arguments that control annotate for the observation labels.

ax : Matplotlib AxesSubplot instance, optional

If given, this subplot is used to plot in instead of a new figure being created.

ret_coords : bool

If True will return the coordinates of the points in the plot. You can use this to add your own annotations.

kwargs :

The keyword arguments passed to plot for the points.


fig : Matplotlib figure instance

If ax is None, the created figure. Otherwise the figure to which ax is connected.

coords : list, optional

If ret_coords is True, return a tuple of arrays (x_coords, y_coords).

See also

Plot partial regression for a set of regressors.


The slope of the fitted line is the that of exog_i in the full multiple regression. The individual points can be used to assess the influence of points on the estimated coefficient.

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