statsmodels.genmod.generalized_estimating_equations.GEE.cluster_list GEE.cluster_list(array) [source] Returns array split into subarrays corresponding to the cluster structure.

statsmodels.discrete.discrete_model.MultinomialResults.bse static MultinomialResults.bse() [source]

statsmodels.sandbox.regression.gmm.IVGMM.calc_weightmatrix IVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covariance matrix wa

statsmodels.genmod.families.links.CDFLink.inverse_deriv CDFLink.inverse_deriv(z) [source] Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.stats.diagnostic.CompareJ.run CompareJ.run(results_x, results_z, attach=True) run J-test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns: tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue : floa

statsmodels.sandbox.stats.multicomp.set_remove_subs statsmodels.sandbox.stats.multicomp.set_remove_subs(ssli) [source] remove sets that are subsets of another set from a list of tuples Parameters: ssli : list of tuples each tuple is considered as a set Returns: part : list of tuples new list with subset tuples removed, it is sorted by set-length of tuples. The list contains original tuples, duplicate elements are not removed. Examples >>> set_remove_subs([(0, 1), (1, 2), (1,

statsmodels.emplike.descriptive.DescStatMV.mv_test_mean DescStatMV.mv_test_mean(mu_array, return_weights=False) [source] Returns -2 x log likelihood and the p-value for a multivariate hypothesis test of the mean Parameters: mu_array : 1d array Hypothesized values for the mean. Must have same number of elements as columns in endog return_weights : bool If True, returns the weights that maximize the likelihood of mu_array. Default is False. Returns: test_results : tuple The log-likelih

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()

statsmodels.tsa.ar_model.ARResults.normalized_cov_params ARResults.normalized_cov_params()

statsmodels.discrete.discrete_model.MultinomialModel.from_formula classmethod MultinomialModel.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas