statsmodels.graphics.regressionplots.plot_ccpr_grid¶

statsmodels.graphics.regressionplots.plot_ccpr_grid(results, exog_idx=None, grid=None, fig=None)[source]¶

Generate CCPR plots against a set of regressors, plot in a grid.

Generates a grid of component and component-plus-residual (CCPR) plots.

Parameters:¶

results : result instance¶

A results instance with exog and params.

exog_idx : None or list of int¶

The indices or column names of the exog used in the plot.

grid : None or tuple of int (nrows, ncols)¶

If grid is given, then it is used for the arrangement of the subplots. If grid is None, then ncol is one, if there are only 2 subplots, and the number of columns is two otherwise.

fig : Figure, optional¶

If given, this figure is simply returned. Otherwise a new figure is created.

Returns:¶

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

Return type:¶

Figure

See also

plot_ccpr

Creates CCPR plot for a single regressor.

Notes

Partial residual plots are formed as:

Res + Betahat(i)*Xi versus Xi

and CCPR adds:

Betahat(i)*Xi versus Xi

References

See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm

Examples

Using the state crime dataset separately plot the effect of the each variable on the on the outcome, murder rate while accounting for the effect of all other variables in the model.

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

>>> fig = plt.figure(figsize=(8, 8))
>>> crime_data = sm.datasets.statecrime.load_pandas()
>>> results = smf.ols('murder ~ hs_grad + urban + poverty + single',
...                   data=crime_data.data).fit()
>>> sm.graphics.plot_ccpr_grid(results, fig=fig)
>>> plt.show()

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