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.iolib.summary.Summary.add_table_2cols Summary.add_table_2cols(res, title=None, gleft=None, gright=None, yname=None, xname=None) [source] add a double table, 2 tables with one column merged horizontally Parameters: res : results instance some required information is directly taken from the result instance title : string or None if None, then a default title is used. gleft : list of tuples elements for the left table, tuples are (name, value) pairs If gleft is None, then a de

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

statsmodels.iolib.table.SimpleTable.reverse SimpleTable.reverse() L.reverse() ? reverse IN PLACE

statsmodels.sandbox.regression.gmm.IVRegressionResults.get_robustcov_results IVRegressionResults.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.discrete.discrete_model.BinaryModel.hessian BinaryModel.hessian(params) The Hessian matrix of the model

statsmodels.tsa.arima_model.ARIMA.geterrors ARIMA.geterrors(params) Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond_mean NonlinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.tsa.arima_model.ARIMAResults.mafreq static ARIMAResults.mafreq() Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.