statsmodels.tsa.vector_ar.var_model.VARResults.plot_acorr VARResults.plot_acorr(nlags=10, linewidth=8) Plot theoretical autocorrelation function

statsmodels.discrete.discrete_model.BinaryResults.aic static BinaryResults.aic()

statsmodels.tsa.ar_model.ARResults.save ARResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data is true and

statsmodels.tsa.ar_model.ARResults.load classmethod ARResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.discrete.discrete_model.Poisson.from_formula classmethod Poisson.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.tsa.arima_model.ARMA.loglike ARMA.loglike(params, set_sigma2=True) [source] Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.stats.weightstats.DescrStatsW.asrepeats DescrStatsW.asrepeats() [source] get array that has repeats given by floor(weights) observations with weight=0 are dropped

statsmodels.sandbox.regression.gmm.IVRegressionResults.bic static IVRegressionResults.bic()

statsmodels.sandbox.regression.gmm.GMMResults.pvalues static GMMResults.pvalues()

statsmodels.discrete.discrete_model.MultinomialModel.score MultinomialModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.