statsmodels.regression.quantile_regression.QuantRegResults.initialize QuantRegResults.initialize(model, params, **kwd)

statsmodels.regression.quantile_regression.QuantRegResults.get_robustcov_results QuantRegResults.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.regression.quantile_regression.QuantRegResults.f_test QuantRegResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypothes

statsmodels.regression.quantile_regression.QuantRegResults.cov_params QuantRegResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column :

statsmodels.regression.quantile_regression.QuantRegResults.conf_int QuantRegResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.quantile_regression.QuantRegResults.compare_f_test QuantRegResults.compare_f_test(restricted) use F test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. Returns: f_value : float test statistic, F distributed p_val

statsmodels.regression.quantile_regression.QuantRegResults.compare_lm_test QuantRegResults.compare_lm_test(restricted, demean=True, use_lr=False) Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bool Fl

statsmodels.regression.quantile_regression.QuantRegResults.compare_lr_test QuantRegResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag indic

statsmodels.regression.quantile_regression.QuantReg.whiten QuantReg.whiten(data) [source] QuantReg model whitener does nothing: returns data.

statsmodels.regression.quantile_regression.QuantReg.score QuantReg.score(params) Score vector of model. The gradient of logL with respect to each parameter.