statsmodels.genmod.generalized_estimating_equations.GEEMargins.tvalues static GEEMargins.tvalues() [source]

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond NonlinearIVGMM.momcond(params)

statsmodels.tsa.arima_model.ARIMA.hessian ARIMA.hessian(params) Compute the Hessian at params, Notes This is a numerical approximation.

statsmodels.genmod.generalized_estimating_equations.GEEResults.bse static GEEResults.bse() [source]

statsmodels.genmod.generalized_linear_model.GLMResults.t_test GLMResults.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

statsmodels.miscmodels.count.PoissonZiGMLE.loglike PoissonZiGMLE.loglike(params)

statsmodels.sandbox.regression.gmm.IVGMM.score_cu IVGMM.score_cu(params, epsilon=None, centered=True)

statsmodels.tsa.vector_ar.var_model.VARResults.resid static VARResults.resid() [source] Residuals of response variable resulting from estimated coefficients

statsmodels.genmod.generalized_estimating_equations.GEEMargins.get_margeff GEEMargins.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) [source] Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for

statsmodels.regression.linear_model.RegressionResults.resid_pearson static RegressionResults.resid_pearson() [source] Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :