statsmodels.sandbox.stats.multicomp.TukeyHSDResults.plot_simultaneous TukeyHSDResults.plot_simultaneous(comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None) [source] Plot a universal confidence interval of each group mean Visiualize significant differences in a plot with one confidence interval per group instead of all pairwise confidence intervals. Parameters: comparison_name : string, optional if provided, plot_intervals will color code all groups that are significan

statsmodels.genmod.families.family.Gaussian.deviance Gaussian.deviance(endog, mu, scale=1.0) [source] Gaussian deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float The deviance function at (endog,mu) as defined below. Notes deviance = sum((endog-mu)**2)

statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

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

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

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

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

statsmodels.sandbox.regression.gmm.IVGMM class statsmodels.sandbox.regression.gmm.IVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] Basic class for instrumental variables estimation using GMM A linear function for the conditional mean is defined as default but the methods should be overwritten by subclasses, currently LinearIVGMM and NonlinearIVGMM are implemented as subclasses. See also LinearIVGMM, NonlinearIVGMM Methods calc_weightmatrix(moms[,

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