statsmodels.tools.tools.clean0 statsmodels.tools.tools.clean0(matrix) [source] Erase columns of zeros: can save some time in pseudoinverse.

statsmodels.duration.hazard_regression.PHRegResults.standard_errors static PHRegResults.standard_errors() [source] Returns the standard errors of the parameter estimates.

statsmodels.regression.linear_model.OLSResults.llf static OLSResults.llf()

statsmodels.nonparametric.kernel_regression.KernelReg.r_squared KernelReg.r_squared() [source] Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fit LinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub m

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.

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

statsmodels.tsa.arima_model.ARMAResults.maroots static ARMAResults.maroots() [source]

statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

statsmodels.tsa.vector_ar.var_model.VARResults.summary VARResults.summary() [source] Compute console output summary of estimates Returns: summary : VARSummary