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

statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.regression.quantile_regression.QuantRegResults.get_robustcov_results QuantRegResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

statsmodels.sandbox.regression.try_catdata.groupsstats_1d statsmodels.sandbox.regression.try_catdata.groupsstats_1d(y, x, labelsunique) [source] use ndimage to get fast mean and variance

statsmodels.robust.robust_linear_model.RLMResults.bcov_scaled static RLMResults.bcov_scaled() [source]

statsmodels.tools.tools.monotone_fn_inverter statsmodels.tools.tools.monotone_fn_inverter(fn, x, vectorized=True, **keywords) Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.gradient_momcond LinearIVGMM.gradient_momcond(params, **kwds) [source]

statsmodels.iolib.foreign.StataReader.variables StataReader.variables() [source] Returns a list of the dataset?s StataVariables objects.

statsmodels.stats.sandwich_covariance.se_cov statsmodels.stats.sandwich_covariance.se_cov(cov) get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters: cov : array_like, square covariance matrix Returns: std : ndarray standard deviation from diagonal of cov