statsmodels.sandbox.regression.gmm.IVRegressionResults.scale static IVRegressionResults.scale()

statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes for all pairs something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : array

statsmodels.discrete.discrete_model.ProbitResults.fittedvalues static ProbitResults.fittedvalues()

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.stats.outliers_influence.OLSInfluence.det_cov_params_not_obsi static OLSInfluence.det_cov_params_not_obsi() [source] (cached attribute) determinant of cov_params of all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.tsa.vector_ar.var_model.VAR.hessian VAR.hessian(params) The Hessian matrix of the model

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

statsmodels.regression.linear_model.OLSResults.HC2_se static OLSResults.HC2_se() See statsmodels.RegressionResults

statsmodels.tools.eval_measures.aicc_sigma statsmodels.tools.eval_measures.aicc_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion (AIC) with small sample correction Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including con

statsmodels.regression.linear_model.GLS.predict GLS.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.