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, **kwargs)[source]¶ Results instance for the QuantReg model
Methods
HC0_se
()See statsmodels.RegressionResults HC1_se
()See statsmodels.RegressionResults HC2_se
()See statsmodels.RegressionResults HC3_se
()See statsmodels.RegressionResults aic
()Akaike’s information criteria. bic
()Bayes’ information criteria. bse
()The standard errors of the parameter estimates. centered_tss
()The total (weighted) sum of squares centered about the mean. 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
()Explained sum of squares. f_pvalue
()p-value of the F-statistic f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()The predicted values for the original (unwhitened) design. fvalue
()F-statistic of the fully specified model. get_prediction
([exog, transform, weights, …])compute prediction results get_robustcov_results
([cov_type, use_t])create new results instance with robust covariance as default initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance. llf
()Log-likelihood of model load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. mse
()mse_model
()Mean squared error the model. mse_resid
()Mean squared error of the residuals. mse_total
()Total mean squared error. nobs
()Number of observations n. normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. prsquared
()pvalues
()The two-tailed p values for the t-stats of the params. remove_data
()remove data arrays, all nobs arrays from result and model resid
()The residuals of the model. resid_pearson
()Residuals, normalized to have unit variance. rsquared
()R-squared of a model with an intercept. rsquared_adj
()Adjusted R-squared. save
(fname[, remove_data])save a pickle of this instance scale
()A scale factor for the covariance matrix. ssr
()Sum of squared (whitened) residuals. 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 t_test_pairwise
(term_name[, method, alpha, …])perform pairwise t_test with multiple testing corrected p-values tvalues
()Return the t-statistic for a given parameter estimate. uncentered_tss
()Uncentered sum of squares. wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns wresid
()The residuals of the transformed/whitened regressand and regressor(s)