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

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.sandbox.distributions.transformed.SquareFunc.inverseminus SquareFunc.inverseminus(x) [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.conf_int IVGMMResults.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.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.nnlf ExpTransf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.tsa.arima_model.ARIMAResults.arfreq static ARIMAResults.arfreq() Returns the frequency of the AR roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.sandbox.regression.gmm.GMM.score_cu GMM.score_cu(params, epsilon=None, centered=True) [source]

statsmodels.duration.hazard_regression.PHRegResults.f_test PHRegResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test

statsmodels.tsa.arima_model.ARIMAResults.pvalues static ARIMAResults.pvalues()

Sandbox This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All s