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1_plot_added_variable_doc = """\
2 Create an added variable plot for a fitted regression model.
4 Parameters
5 ----------
6 %(extra_params_doc)sfocus_exog : int or string
7 The column index of exog, or a variable name, indicating the
8 variable whose role in the regression is to be assessed.
9 resid_type : str
10 The type of residuals to use for the dependent variable. If
11 None, uses `resid_deviance` for GLM/GEE and `resid` otherwise.
12 use_glm_weights : bool
13 Only used if the model is a GLM or GEE. If True, the
14 residuals for the focus predictor are computed using WLS, with
15 the weights obtained from the IRLS calculations for fitting
16 the GLM. If False, unweighted regression is used.
17 fit_kwargs : dict, optional
18 Keyword arguments to be passed to fit when refitting the
19 model.
20 ax: Axes
21 Matplotlib Axes instance
23 Returns
24 -------
25 Figure
26 A matplotlib figure instance.
27"""
29_plot_partial_residuals_doc = """\
30 Create a partial residual, or 'component plus residual' plot for a
31 fitted regression model.
33 Parameters
34 ----------
35 %(extra_params_doc)sfocus_exog : int or string
36 The column index of exog, or variable name, indicating the
37 variable whose role in the regression is to be assessed.
38 ax: Axes
39 Matplotlib Axes instance
41 Returns
42 -------
43 Figure
44 A matplotlib figure instance.
45"""
47_plot_ceres_residuals_doc = """\
48 Conditional Expectation Partial Residuals (CERES) plot.
50 Produce a CERES plot for a fitted regression model.
52 Parameters
53 ----------
54 %(extra_params_doc)s
55 focus_exog : {int, str}
56 The column index of results.model.exog, or the variable name,
57 indicating the variable whose role in the regression is to be
58 assessed.
59 frac : float
60 Lowess tuning parameter for the adjusted model used in the
61 CERES analysis. Not used if `cond_means` is provided.
62 cond_means : array_like, optional
63 If provided, the columns of this array span the space of the
64 conditional means E[exog | focus exog], where exog ranges over
65 some or all of the columns of exog (other than the focus exog).
66 ax : matplotlib.Axes instance, optional
67 The axes on which to draw the plot. If not provided, a new
68 axes instance is created.
70 Returns
71 -------
72 Figure
73 The figure on which the partial residual plot is drawn.
75 Notes
76 -----
77 `cond_means` is intended to capture the behavior of E[x1 |
78 x2], where x2 is the focus exog and x1 are all the other exog
79 variables. If all the conditional mean relationships are
80 linear, it is sufficient to set cond_means equal to the focus
81 exog. Alternatively, cond_means may consist of one or more
82 columns containing functional transformations of the focus
83 exog (e.g. x2^2) that are thought to capture E[x1 | x2].
85 If nothing is known or suspected about the form of E[x1 | x2],
86 set `cond_means` to None, and it will be estimated by
87 smoothing each non-focus exog against the focus exog. The
88 values of `frac` control these lowess smooths.
90 If cond_means contains only the focus exog, the results are
91 equivalent to a partial residual plot.
93 If the focus variable is believed to be independent of the
94 other exog variables, `cond_means` can be set to an (empty)
95 nx0 array.
97 References
98 ----------
99 .. [1] RD Cook and R Croos-Dabrera (1998). Partial residual plots
100 in generalized linear models. Journal of the American
101 Statistical Association, 93:442.
103 .. [2] RD Cook (1993). Partial residual plots. Technometrics 35:4.
105 Examples
106 --------
107 Using a model built from the the state crime dataset, make a CERES plot with
108 the rate of Poverty as the focus variable.
110 >>> import statsmodels.api as sm
111 >>> import matplotlib.pyplot as plt
112 >>> import statsmodels.formula.api as smf
113 >>> from statsmodels.graphics.regressionplots import plot_ceres_residuals
115 >>> crime_data = sm.datasets.statecrime.load_pandas()
116 >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single',
117 ... data=crime_data.data).fit()
118 >>> plot_ceres_residuals(results, 'poverty')
119 >>> plt.show()
121 .. plot:: plots/graphics_regression_ceres_residuals.py
122"""
125_plot_influence_doc = """\
126 Plot of influence in regression. Plots studentized resids vs. leverage.
128 Parameters
129 ----------
130 {extra_params_doc}
131 external : bool
132 Whether to use externally or internally studentized residuals. It is
133 recommended to leave external as True.
134 alpha : float
135 The alpha value to identify large studentized residuals. Large means
136 abs(resid_studentized) > t.ppf(1-alpha/2, dof=results.df_resid)
137 criterion : str {{'DFFITS', 'Cooks'}}
138 Which criterion to base the size of the points on. Options are
139 DFFITS or Cook's D.
140 size : float
141 The range of `criterion` is mapped to 10**2 - size**2 in points.
142 plot_alpha : float
143 The `alpha` of the plotted points.
144 ax : AxesSubplot
145 An instance of a matplotlib Axes.
146 **kwargs
147 Additional parameters passed through to `plot`.
149 Returns
150 -------
151 Figure
152 The matplotlib figure that contains the Axes.
154 Notes
155 -----
156 Row labels for the observations in which the leverage, measured by the
157 diagonal of the hat matrix, is high or the residuals are large, as the
158 combination of large residuals and a high influence value indicates an
159 influence point. The value of large residuals can be controlled using the
160 `alpha` parameter. Large leverage points are identified as
161 hat_i > 2 * (df_model + 1)/nobs.
163 Examples
164 --------
165 Using a model built from the the state crime dataset, plot the influence in
166 regression. Observations with high leverage, or large residuals will be
167 labeled in the plot to show potential influence points.
169 >>> import statsmodels.api as sm
170 >>> import matplotlib.pyplot as plt
171 >>> import statsmodels.formula.api as smf
173 >>> crime_data = sm.datasets.statecrime.load_pandas()
174 >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single',
175 ... data=crime_data.data).fit()
176 >>> sm.graphics.influence_plot(results)
177 >>> plt.show()
179 .. plot:: plots/graphics_regression_influence.py
180 """
183_plot_leverage_resid2_doc = """\
184 Plot leverage statistics vs. normalized residuals squared
186 Parameters
187 ----------
188 results : results instance
189 A regression results instance
190 alpha : float
191 Specifies the cut-off for large-standardized residuals. Residuals
192 are assumed to be distributed N(0, 1) with alpha=alpha.
193 ax : Axes
194 Matplotlib Axes instance
195 **kwargs
196 Additional parameters passed the plot command.
198 Returns
199 -------
200 Figure
201 A matplotlib figure instance.
203 Examples
204 --------
205 Using a model built from the the state crime dataset, plot the leverage
206 statistics vs. normalized residuals squared. Observations with
207 Large-standardized Residuals will be labeled in the plot.
209 >>> import statsmodels.api as sm
210 >>> import matplotlib.pyplot as plt
211 >>> import statsmodels.formula.api as smf
213 >>> crime_data = sm.datasets.statecrime.load_pandas()
214 >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single',
215 ... data=crime_data.data).fit()
216 >>> sm.graphics.plot_leverage_resid2(results)
217 >>> plt.show()
219 .. plot:: plots/graphics_regression_leverage_resid2.py
220 """