statsmodels.tsa.vector_ar.var_model.VARResults.cov_ybar VARResults.cov_ybar() [source] Asymptotically consistent estimate of covariance of the sample mean Notes Lutkepohl Proposition 3.3

statsmodels.discrete.discrete_model.MultinomialResults.pred_table MultinomialResults.pred_table() [source] Returns the J x J prediction table. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.

statsmodels.stats.diagnostic.CompareJ class statsmodels.stats.diagnostic.CompareJ J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.sandbox.regression.gmm.IVGMMResults.predict IVGMMResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data str

statsmodels.discrete.discrete_model.MultinomialResults.predict MultinomialResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pa

statsmodels.discrete.discrete_model.MultinomialResults.save MultinomialResults.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. Not

statsmodels.stats.weightstats.DescrStatsW.get_compare DescrStatsW.get_compare(other, weights=None) [source] return an instance of CompareMeans with self and other Parameters: other : array_like or instance of DescrStatsW If array_like then this creates an instance of DescrStatsW with the given weights. weights : None or array weights are only used if other is not an instance of DescrStatsW Returns: cm : instance of CompareMeans the instance has self attached as d1 and other as d2.

statsmodels.duration.hazard_regression.PHRegResults.score_residuals static PHRegResults.score_residuals() [source] A matrix containing the score residuals.

statsmodels.duration.hazard_regression.PHRegResults.normalized_cov_params PHRegResults.normalized_cov_params()

statsmodels.regression.linear_model.RegressionResults.mse_resid static RegressionResults.mse_resid() [source]