statsmodels.discrete.discrete_model.DiscreteResults.get_margeff DiscreteResults.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 each r

statsmodels.duration.hazard_regression.PHReg.loglike PHReg.loglike(params) [source] Returns the log partial likelihood function evaluated at params.

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

statsmodels.stats.power.NormalIndPower.solve_power NormalIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] solve for any one parameter of the power of a two sample z-test for z-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviatio

statsmodels.miscmodels.count.PoissonZiGMLE.hessian PoissonZiGMLE.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonZiGMLE.initialize PoissonZiGMLE.initialize()

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

statsmodels.discrete.discrete_model.BinaryResults class statsmodels.discrete.discrete_model.BinaryResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for binary data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterio

statsmodels.sandbox.regression.gmm.LinearIVGMM.get_error LinearIVGMM.get_error(params)

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.score NonlinearIVGMM.score(params, weights, **kwds) [source]