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

statsmodels.regression.quantile_regression.QuantRegResults.HC2_se static QuantRegResults.HC2_se() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.sample_acorr VARResults.sample_acorr(nlags=1) [source]

statsmodels.robust.norms.AndrewWave.psi_deriv AndrewWave.psi_deriv(z) [source] The derivative of Andrew?s wave psi function Notes Used to estimate the robust covariance matrix.

statsmodels.sandbox.stats.multicomp.rejectionline statsmodels.sandbox.stats.multicomp.rejectionline(n, alpha=0.5) [source] reference line for rejection in multiple tests Not used anymore from: section 3.2, page 60

statsmodels.regression.quantile_regression.QuantRegResults.HC3_se static QuantRegResults.HC3_se() [source]

statsmodels.sandbox.distributions.extras.SkewNorm2_gen class statsmodels.sandbox.distributions.extras.SkewNorm2_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None) [source] univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds)

statsmodels.robust.robust_linear_model.RLMResults class statsmodels.robust.robust_linear_model.RLMResults(model, params, normalized_cov_params, scale) [source] Class to contain RLM results Returns: **Attributes** : bcov_scaled : array p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1) where k = 1 + (df_model +1)/nobs * var_psiprime/m**