statsmodels.regression.quantile_regression.QuantRegResults
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class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)
[source] -
Results instance for the QuantReg model
Methods
HC0_se
()HC1_se
()HC2_se
()HC3_se
()aic
()bic
()bse
()centered_tss
()compare_f_test
(restricted)use F test to test whether restricted model is correct compare_lm_test
(restricted[, demean, use_lr])Use Lagrange Multiplier test to test whether restricted model is correct compare_lr_test
(restricted[, large_sample])Likelihood ratio test to test whether restricted model is correct condition_number
()Return condition number of exogenous matrix. conf_int
([alpha, cols])Returns the confidence interval of the fitted parameters. cov_HC0
()See statsmodels.RegressionResults cov_HC1
()See statsmodels.RegressionResults cov_HC2
()See statsmodels.RegressionResults cov_HC3
()See statsmodels.RegressionResults cov_params
([r_matrix, column, scale, cov_p, ...])Returns the variance/covariance matrix. eigenvals
()Return eigenvalues sorted in decreasing order. ess
()f_pvalue
()f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()fvalue
()get_robustcov_results
([cov_type, use_t])create new results instance with robust covariance as default initialize
(model, params, **kwd)llf
()load
(fname)load a pickle, (class method) mse
()mse_model
()mse_resid
()mse_total
()nobs
()normalized_cov_params
()predict
([exog, transform])Call self.model.predict with self.params as the first argument. prsquared
()pvalues
()remove_data
()remove data arrays, all nobs arrays from result and model resid
()resid_pearson
()Residuals, normalized to have unit variance. rsquared
()rsquared_adj
()save
(fname[, remove_data])save a pickle of this instance scale
()ssr
()summary
([yname, xname, title, alpha])Summarize the Regression Results summary2
([yname, xname, title, alpha, ...])Experimental summary function to summarize the regression results t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q tvalues
()Return the t-statistic for a given parameter estimate. uncentered_tss
()wald_test
(r_matrix[, cov_p, scale, invcov, ...])Compute a Wald-test for a joint linear hypothesis. wresid
()Attributes
use_t
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