statsmodels.tsa.stattools.grangercausalitytests statsmodels.tsa.stattools.grangercausalitytests(x, maxlag, addconst=True, verbose=True) [source] four tests for granger non causality of 2 timeseries all four tests give similar results params_ftest and ssr_ftest are equivalent based on F test which is identical to lmtest:grangertest in R Parameters: x : array, 2d, (nobs,2) data for test whether the time series in the second column Granger causes the time series in the first column maxlag :

statsmodels.duration.hazard_regression.PHRegResults.save PHRegResults.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 rem

statsmodels.tsa.arima_model.ARMAResults.conf_int ARMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Default?

statsmodels.tsa.arima_model.ARMAResults.remove_data ARMAResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is access

statsmodels.discrete.discrete_model.Probit.jac Probit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Probit model Jacobian for each observation Parameters: params : array-like The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations Where . This simplification comes from the fact that the normal distribution

statsmodels.stats.weightstats.DescrStatsW.corrcoef static DescrStatsW.corrcoef() [source] weighted correlation with default ddof assumes variables in columns and observations in rows

statsmodels.tsa.vector_ar.var_model.VARResults.test_causality VARResults.test_causality(equation, variables, kind='f', signif=0.05, verbose=True) [source] Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables Parameters: equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {?f?, ?wald?} Perf

statsmodels.tsa.ar_model.ARResults.llf static ARResults.llf()

statsmodels.stats.weightstats.CompareMeans class statsmodels.stats.weightstats.CompareMeans(d1, d2) [source] class for two sample comparison The tests and the confidence interval work for multi-endpoint comparison: If d1 and d2 have the same number of rows, then each column of the data in d1 is compared with the corresponding column in d2. Parameters: d1, d2 : instances of DescrStatsW Notes The result for the statistical tests and the confidence interval are independent of the user specifie

statsmodels.iolib.table.SimpleTable.label_cells SimpleTable.label_cells(func) [source] Return None. Labels cells based on func. If func(cell) is None then its datatype is not changed; otherwise it is set to func(cell).