reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/graphics/_regressionplots_doc.py

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+_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 : str
+        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
+        Matplotlib Axes instance
+
+    Returns
+    -------
+    Figure
+        A matplotlib figure instance.
+"""
+
+_plot_partial_residuals_doc = """\
+    Create a partial residual, or 'component plus residual' plot for a
+    fitted 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
+        Matplotlib Axes instance
+
+    Returns
+    -------
+    Figure
+        A matplotlib figure instance.
+"""
+
+_plot_ceres_residuals_doc = """\
+    Conditional Expectation Partial Residuals (CERES) plot.
+
+    Produce a CERES plot for a fitted regression model.
+
+    Parameters
+    ----------
+    %(extra_params_doc)s
+    focus_exog : {int, str}
+        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
+    -------
+    Figure
+        The figure on which the partial residual plot is drawn.
+
+    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
+    ----------
+    .. [1] RD Cook and R Croos-Dabrera (1998).  Partial residual plots
+       in generalized linear models.  Journal of the American
+       Statistical Association, 93:442.
+
+    .. [2] RD Cook (1993). Partial residual plots.  Technometrics 35:4.
+
+    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 : AxesSubplot
+        An instance of a matplotlib Axes.
+    **kwargs
+        Additional parameters passed through to `plot`.
+
+    Returns
+    -------
+    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 = """\
+    Plot 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
+        Matplotlib Axes instance
+    **kwargs
+        Additional parameters passed the plot command.
+
+    Returns
+    -------
+    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
+    """