statsmodels.regression.linear_model.OLSResults.save OLSResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_dat

statsmodels.sandbox.tsa.fftarma.ArmaFft.acf ArmaFft.acf(nobs=None) 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_acovf, acf, acovf

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.var ExpTransf_gen.var(*args, **kwds) Variance 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=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution

statsmodels.tsa.vector_ar.var_model.VARProcess.plotsim VARProcess.plotsim(steps=1000) [source] Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.discrete.discrete_model.ProbitResults.get_margeff ProbitResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?al

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.sandbox.distributions.extras.SkewNorm2_gen.rvs SkewNorm2_gen.rvs(*args, **kwds) Random variates of given type. 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=0). scale : array_like, optional Scale parameter (default=1). size : int or tuple of ints, optional Defining number of random variates (default=1). Returns:

statsmodels.robust.robust_linear_model.RLMResults.llf static RLMResults.llf()

statsmodels.discrete.discrete_model.ProbitResults.resid_pearson static ProbitResults.resid_pearson() Pearson residuals Notes Pearson residuals are defined to be where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_split static GEEResults.resid_split() [source] Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.