statsmodels.discrete.discrete_model.Probit.fit Probit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional Th

statsmodels.regression.linear_model.RegressionResults.cov_HC2 static RegressionResults.cov_HC2() [source] See statsmodels.RegressionResults

statsmodels.stats.diagnostic.unitroot_adf statsmodels.stats.diagnostic.unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False)

statsmodels.discrete.discrete_model.ProbitResults.llr static ProbitResults.llr()

statsmodels.sandbox.regression.gmm.IVGMMResults.t_test IVGMMResults.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 tuple of a

statsmodels.discrete.discrete_model.NegativeBinomialResults.resid static NegativeBinomialResults.resid() Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.discrete.discrete_model.DiscreteModel.fit DiscreteModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str

statsmodels.sandbox.regression.gmm.GMMResults.load classmethod GMMResults.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 :