statsmodels.graphics.tsaplots.plot_pacf statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, **kwargs) [source] Plot the partial autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters: x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional Arra

statsmodels.graphics.tsaplots.plot_acf statsmodels.graphics.tsaplots.plot_acf(x, ax=None, lags=None, alpha=0.05, use_vlines=True, unbiased=False, fft=False, **kwargs) [source] Plot the autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters: x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional A

statsmodels.graphics.tsaplots.month_plot statsmodels.graphics.tsaplots.month_plot(x, dates=None, ylabel=None, ax=None) [source] Seasonal plot of monthly data Parameters: x : array-like Seasonal data to plot. If dates is None, x must be a pandas object with a PeriodIndex or DatetimeIndex with a monthly frequency. dates : array-like, optional If x is not a pandas object, then dates must be supplied. ylabel : str, optional The label for the y-axis. Will attempt to use the name attribute o

statsmodels.graphics.regressionplots.plot_regress_exog statsmodels.graphics.regressionplots.plot_regress_exog(results, exog_idx, fig=None) [source] Plot regression results against one regressor. This plots four graphs in a 2 by 2 figure: ?endog versus exog?, ?residuals versus exog?, ?fitted versus exog? and ?fitted plus residual versus exog? Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes exog_idx : int index of regressor in exog

statsmodels.graphics.regressionplots.plot_partregress statsmodels.graphics.regressionplots.plot_partregress(endog, exog_i, exog_others, data=None, title_kwargs={}, obs_labels=True, label_kwargs={}, ax=None, ret_coords=False, **kwargs) [source] Plot partial regression for a single regressor. Parameters: endog : ndarray or string endogenous or response variable. If string is given, you can use a arbitrary translations as with a formula. exog_i : ndarray or string exogenous, explanatory var

statsmodels.graphics.regressionplots.plot_leverage_resid2 statsmodels.graphics.regressionplots.plot_leverage_resid2(results, alpha=0.05, label_kwargs={}, ax=None, **kwargs) [source] 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. label_kwargs : dict The keywords to pass to

statsmodels.graphics.regressionplots.plot_fit statsmodels.graphics.regressionplots.plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs) [source] Plot fit against one regressor. This creates one graph with the scatterplot of observed values compared to fitted values. Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes x_var : int or str Name or index of regressor in exog matrix. y_true : array_like (optional) If this is not N

statsmodels.graphics.regressionplots.plot_ccpr statsmodels.graphics.regressionplots.plot_ccpr(results, exog_idx, ax=None) [source] Plot CCPR against one regressor. Generates a CCPR (component and component-plus-residual) plot. Parameters: results : result instance A regression results instance. exog_idx : int or string Exogenous, explanatory variable. If string is given, it should be the variable name that you want to use, and you can use arbitrary translations as with a formula. ax : M

statsmodels.graphics.regressionplots.influence_plot statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source] Plot of influence in regression. Plots studentized resids vs. leverage. Parameters: results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha va

statsmodels.graphics.regressionplots.abline_plot statsmodels.graphics.regressionplots.abline_plot(intercept=None, slope=None, horiz=None, vert=None, model_results=None, ax=None, **kwargs) [source] Plots a line given an intercept and slope. intercept : float The intercept of the line slope : float The slope of the line horiz : float or array-like Data for horizontal lines on the y-axis vert : array-like Data for verterical lines on the x-axis model_results : statsmodels results insta