statsmodels.regression.mixed_linear_model.MixedLMResults.t_test MixedLMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.sandbox.regression.gmm.IVRegressionResults.condition_number static IVRegressionResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.discrete.discrete_model.NegativeBinomialResults.llf static NegativeBinomialResults.llf()

statsmodels.duration.hazard_regression.PHReg.information PHReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.genmod.families.family.InverseGaussian.fitted InverseGaussian.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.discrete.discrete_model.NegativeBinomialResults.pvalues static NegativeBinomialResults.pvalues()

statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.regression.quantile_regression.QuantRegResults.get_robustcov_results QuantRegResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

statsmodels.tsa.arima_model.ARIMA.geterrors ARIMA.geterrors(params) Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.discrete.discrete_model.BinaryModel.hessian BinaryModel.hessian(params) The Hessian matrix of the model