B ¤ŠãZžã@s@ddlmZddlZddlmZeddgƒZGdd„deƒZdS)é)Ú namedtupleN)ÚBunchÚ_MinimalWLSModelÚweightsc@s$eZdZdZd dd„Zd dd„ZdS) Ú _MinimalWLSaŠ Minimal implementation of WLS optimized for performance. Parameters ---------- endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where `nobs` is the number of observations and `k` is the number of regressors. An intercept is not included by default and should be added by the user. See :func:`statsmodels.tools.add_constant`. weights : array-like, optional 1d array of weights. If you supply 1/W then the variables are pre- multiplied by 1/sqrt(W). If no weights are supplied the default value is 1 and WLS reults are the same as OLS. Notes ----- Need resid, scale, fittedvalues, model.weights! history['scale'].append(tmp_results.scale) if conv == 'dev': history['deviance'].append(self.deviance(tmp_results)) elif conv == 'sresid': history['sresid'].append(tmp_results.resid/tmp_results.scale) elif conv == 'weights': history['weights'].append(tmp_results.model.weights) Does not perform and checks on the input data çð?cCsV||_||_||_t |¡}|||_t |¡r<|||_n|dd…df||_dS)N)ÚendogÚexogrÚnpZsqrtÚwendogZisscalarÚwexog)Úselfrr rZw_half©rús