Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model.
Parameters: | focus_exog : integer or string
frac : float
cond_means : array-like, optional
ax : matplotlib.Axes instance, optional
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Returns: | fig : matplotlib.Figure instance
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Notes
cond_means is intended to capture the behavior of E[x1 | x2], where x2 is the focus exog and x1 are all the other exog variables. If all the conditional mean relationships are linear, it is sufficient to set cond_means equal to the focus exog. Alternatively, cond_means may consist of one or more columns containing functional transformations of the focus exog (e.g. x2^2) that are thought to capture E[x1 | x2].
If nothing is known or suspected about the form of E[x1 | x2], set cond_means to None, and it will be estimated by smoothing each non-focus exog against the focus exog. The values of frac control these lowess smooths.
If cond_means contains only the focus exog, the results are equivalent to a partial residual plot.
If the focus variable is believed to be independent of the other exog variables, cond_means can be set to an (empty) nx0 array.
References
RD Cook and R Croos-Dabrera (1998). Partial residual plots in generalized linear models. Journal of the American Statistical Association, 93:442.
RD Cook (1993). Partial residual plots. Technometrics 35:4.