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

statsmodels.stats.diagnostic.kstest_normal statsmodels.stats.diagnostic.kstest_normal(x, pvalmethod='approx') Lillifors test for normality, Kolmogorov Smirnov test with estimated mean and variance Parameters: x : array_like, 1d data series, sample pvalmethod : ?approx?, ?table? ?approx? uses the approximation formula of Dalal and Wilkinson, valid for pvalues < 0.1. If the pvalue is larger than 0.1, then the result of table is returned ?table? uses the table from Dalal and Wilkinson, w

statsmodels.sandbox.regression.gmm.IVGMM.start_weights IVGMM.start_weights(inv=True) [source]

statsmodels.regression.mixed_linear_model.MixedLMResults.wald_test MixedLMResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tu

statsmodels.discrete.discrete_model.Poisson.initialize Poisson.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.genmod.families.links.Power.deriv2 Power.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.discrete.discrete_model.NegativeBinomial.jac NegativeBinomial.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7

statsmodels.discrete.discrete_model.BinaryModel.fit BinaryModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional T

statsmodels.sandbox.regression.gmm.GMM.start_weights GMM.start_weights(inv=True) [source]

statsmodels.genmod.cov_struct.GlobalOddsRatio class statsmodels.genmod.cov_struct.GlobalOddsRatio(endog_type) [source] Estimate the global odds ratio for a GEE with ordinal or nominal data. Notes The following data structures are calculated in the class: ?ibd? is a list whose i^th element ibd[i] is a sequence of integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. cpp is a dictionary where cpp[group] is a map from cut-point pa