statsmodels.genmod.generalized_estimating_equations.GEEResults.initialize GEEResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARMAResults.normalized_cov_params ARMAResults.normalized_cov_params()

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

statsmodels.genmod.generalized_linear_model.GLMResults.bic static GLMResults.bic() [source]

statsmodels.sandbox.regression.try_catdata.convertlabels statsmodels.sandbox.regression.try_catdata.convertlabels(ys, indices=None) [source] convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels

statsmodels.discrete.discrete_model.BinaryResults.f_test BinaryResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.median TransfTwo_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ 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) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val

statsmodels.miscmodels.count.PoissonOffsetGMLE.information PoissonOffsetGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.RegressionResults.compare_lr_test RegressionResults.compare_lr_test(restricted, large_sample=False) [source] Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag