statsmodels.discrete.discrete_model.BinaryResults.prsquared static BinaryResults.prsquared()

statsmodels.sandbox.regression.gmm.IVRegressionResults.f_pvalue static IVRegressionResults.f_pvalue()

statsmodels.genmod.families.family.Gamma.starting_mu Gamma.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

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

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.tsa.arima_model.ARIMA.loglike ARIMA.loglike(params, set_sigma2=True) Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.tsa.arima_model.ARMA.information ARMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.genmod.families.family.Family.weights Family.weights(mu) [source] Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

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

statsmodels.tsa.ar_model.ARResults.bic static ARResults.bic() [source]