statsmodels.genmod.generalized_estimating_equations.GEEResults class statsmodels.genmod.generalized_estimating_equations.GEEResults(model, params, cov_params, scale, cov_type='robust', use_t=False, **kwds) [source] This class summarizes the fit of a marginal regression model using GEE. Returns: **Attributes** : cov_params_default : ndarray default covariance of the parameter estimates. Is chosen among one of the following three based on cov_type cov_robust : ndarray covariance of the par

statsmodels.nonparametric.kde.KDEUnivariate.cdf static KDEUnivariate.cdf() [source] Returns the cumulative distribution function evaluated at the support. Notes Will not work if fit has not been called.

statsmodels.discrete.discrete_model.LogitResults.tvalues static LogitResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.MultinomialModel.information MultinomialModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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

statsmodels.discrete.discrete_model.CountResults.normalized_cov_params CountResults.normalized_cov_params()

statsmodels.discrete.discrete_model.Poisson.information Poisson.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.stats.multicomp.StepDown.stepdown StepDown.stepdown(indices) [source]

statsmodels.duration.hazard_regression.PHReg.breslow_hessian PHReg.breslow_hessian(params) [source] Returns the Hessian of the log partial likelihood evaluated at params, using the Breslow method to handle tied times.

statsmodels.stats.weightstats.CompareMeans.std_meandiff_pooledvar static CompareMeans.std_meandiff_pooledvar() [source] variance assuming equal variance in both data sets