_plot_added_variable_doc = """\ Create an added variable plot for a fitted regression model. Parameters ---------- %(extra_params_doc)sfocus_exog : int or string The column index of exog, or a variable name, indicating the variable whose role in the regression is to be assessed. resid_type : string The type of residuals to use for the dependent variable. If None, uses `resid_deviance` for GLM/GEE and `resid` otherwise. use_glm_weights : bool Only used if the model is a GLM or GEE. If True, the residuals for the focus predictor are computed using WLS, with the weights obtained from the IRLS calculations for fitting the GLM. If False, unweighted regression is used. fit_kwargs : dict, optional Keyword arguments to be passed to fit when refitting the model. ax : Axes instance Matplotlib Axes instance Returns ------- fig : matplotlib Figure A matplotlib figure instance. """ _plot_partial_residuals_doc = """\ Create a partial residual, or 'component plus residual' plot for a fited regression model. Parameters ---------- %(extra_params_doc)sfocus_exog : int or string The column index of exog, or variable name, indicating the variable whose role in the regression is to be assessed. ax : Axes instance Matplotlib Axes instance Returns ------- fig : matplotlib Figure A matplotlib figure instance. """ _plot_ceres_residuals_doc = """\ Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model. Parameters ---------- %(extra_params_doc)sfocus_exog : integer or string The column index of results.model.exog, or the variable name, indicating the variable whose role in the regression is to be assessed. frac : float Lowess tuning parameter for the adjusted model used in the CERES analysis. Not used if `cond_means` is provided. cond_means : array-like, optional If provided, the columns of this array span the space of the conditional means E[exog | focus exog], where exog ranges over some or all of the columns of exog (other than the focus exog). ax : matplotlib.Axes instance, optional The axes on which to draw the plot. If not provided, a new axes instance is created. Returns ------- fig : matplotlib.Figure instance The figure on which the partial residual plot is drawn. 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. 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. Examples -------- Using a model built from the the state crime dataset, make a CERES plot with the rate of Poverty as the focus variable. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> from statsmodels.graphics.regressionplots import plot_ceres_residuals >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> plot_ceres_residuals(results, 'poverty') >>> plt.show() .. plot:: plots/graphics_regression_ceres_residuals.py """ _plot_influence_doc = """\ Plot of influence in regression. Plots studentized resids vs. leverage. Parameters ---------- %(extra_params_doc)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. Examples -------- Using a model built from the the state crime dataset, plot the influence in regression. Observations with high leverage, or large residuals will be labeled in the plot to show potential influence points. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> sm.graphics.influence_plot(results) >>> plt.show() .. plot:: plots/graphics_regression_influence.py """ _plot_leverage_resid2_doc = """\ Plots leverage statistics vs. normalized residuals squared Parameters ---------- results : results instance A regression results instance alpha : float Specifies the cut-off for large-standardized residuals. Residuals are assumed to be distributed N(0, 1) with alpha=alpha. ax : Axes instance Matplotlib Axes instance Returns ------- fig : matplotlib Figure A matplotlib figure instance. Examples -------- Using a model built from the the state crime dataset, plot the leverage statistics vs. normalized residuals squared. Observations with Large-standardized Residuals will be labeled in the plot. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> sm.graphics.plot_leverage_resid2(results) >>> plt.show() .. plot:: plots/graphics_regression_leverage_resid2.py """