statsmodels.stats.diagnostic.acorr_breush_godfrey statsmodels.stats.diagnostic.acorr_breush_godfrey(results, nlags=None, store=False) Breush Godfrey Lagrange Multiplier tests for residual autocorrelation Parameters: results : Result instance Estimation results for which the residuals are tested for serial correlation nlags : int Number of lags to include in the auxiliary regression. (nlags is highest lag) store : bool If store is true, then an additional class instance that contains in

statsmodels.robust.robust_linear_model.RLMResults.load classmethod RLMResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tools.eval_measures.meanabs statsmodels.tools.eval_measures.meanabs(x1, x2, axis=0) [source] mean absolute error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: meanabs : ndarray or float mean absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert t

statsmodels.tsa.arima_process.ArmaProcess.acf ArmaProcess.acf(nobs=None) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_aco

statsmodels.tools.eval_measures.medianabs statsmodels.tools.eval_measures.medianabs(x1, x2, axis=0) [source] median absolute error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: medianabs : ndarray or float median absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to

statsmodels.tools.eval_measures.mse statsmodels.tools.eval_measures.mse(x1, x2, axis=0) [source] mean squared error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: mse : ndarray or float mean squared error along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert the input. Whether t

statsmodels.tools.eval_measures.rmse statsmodels.tools.eval_measures.rmse(x1, x2, axis=0) [source] root mean squared error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: rmse : ndarray or float root mean squared error along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert the inp

statsmodels.regression.linear_model.RegressionResults.scale static RegressionResults.scale() [source]

statsmodels.discrete.discrete_model.ProbitResults.resid_response static ProbitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.tools.numdiff.approx_hess_cs statsmodels.tools.numdiff.approx_hess_cs(x, f, epsilon=None, args=(), kwargs={}) [source] Calculate Hessian with complex-step derivative approximation Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chose