statsmodels.genmod.cov_struct.Nested.initialize Nested.initialize(model) [source] Called on the first call to update ilabels is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. designx is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY.

statsmodels.discrete.discrete_model.MultinomialResults.predict MultinomialResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pa

statsmodels.robust.norms.LeastSquares.rho LeastSquares.rho(z) [source] The least squares estimator rho function Parameters: z : array 1d array Returns: rho : array rho(z) = (1/2.)*z**2

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.std TransfTwo_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

statsmodels.sandbox.regression.gmm.IVRegressionResults.t_test IVRegressionResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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. tuple

statsmodels.stats.weightstats.DescrStatsW.corrcoef static DescrStatsW.corrcoef() [source] weighted correlation with default ddof assumes variables in columns and observations in rows

statsmodels.tsa.vector_ar.var_model.VARResults.test_causality VARResults.test_causality(equation, variables, kind='f', signif=0.05, verbose=True) [source] Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables Parameters: equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {?f?, ?wald?} Perf

statsmodels.tsa.ar_model.ARResults.llf static ARResults.llf()

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary_frame GEEMargins.summary_frame(alpha=0.05) [source] Returns a DataFrame summarizing the marginal effects. Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: frame : DataFrames A DataFrame summarizing the marginal effects.

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