statsmodels.discrete.discrete_model.NegativeBinomialResults.get_margeff NegativeBinomialResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) 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.sandbox.regression.gmm.IVRegressionResults.nobs static IVRegressionResults.nobs()

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix

statsmodels.miscmodels.tmodel.TLinearModel.information TLinearModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ma ArmaFft.arma2ma(nobs=None)

statsmodels.genmod.generalized_linear_model.GLMResults.resid_response static GLMResults.resid_response() [source]

statsmodels.miscmodels.count.PoissonZiGMLE.score_obs PoissonZiGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.sandbox.regression.gmm.IV2SLS.loglike IV2SLS.loglike(params) Log-likelihood of model.

statsmodels.discrete.discrete_model.Poisson.score_obs Poisson.score_obs(params) [source] Poisson model Jacobian of the log-likelihood for each observation Parameters: params : array-like The parameters of the model Returns: score : ndarray (nobs, k_vars) The score vector of the model evaluated at params Notes for observations where the loglinear model is assumed