, 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.


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.