statsmodels.regression.quantile_regression.QuantRegResults¶

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

`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 a set of linear restrictions.
`compare_lr_test`(restricted[, large_sample])	Likelihood ratio test to test whether restricted model is correct.
`conf_int`([alpha, cols])	Compute the confidence interval of the fitted parameters.
`cov_params`([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
`f_test`(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
`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, **kwargs)	Initialize (possibly re-initialize) a Results instance.
`load`(fname)	Load a pickled results instance
`normalized_cov_params`()	See specific model class docstring
`predict`([exog, transform])	Call self.model.predict with self.params as the first argument.
`remove_data`()	Remove data arrays, all nobs arrays from result and model.
`save`(fname[, remove_data])	Save a pickle of this instance.
`scale`()	A scale factor for the covariance matrix.
`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.
`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.

Properties

`HC0_se`
`HC1_se`
`HC2_se`
`HC3_se`
`aic`
`bic`
`bse`	The standard errors of the parameter estimates.
`centered_tss`
`condition_number`	Return condition number of exogenous matrix.
`cov_HC0`	Heteroscedasticity robust covariance matrix.
`cov_HC1`	Heteroscedasticity robust covariance matrix.
`cov_HC2`	Heteroscedasticity robust covariance matrix.
`cov_HC3`	Heteroscedasticity robust covariance matrix.
`eigenvals`	Return eigenvalues sorted in decreasing order.
`ess`	The explained sum of squares.
`f_pvalue`	The p-value of the F-statistic.
`fittedvalues`	The predicted values for the original (unwhitened) design.
`fvalue`	F-statistic of the fully specified model.
`llf`
`mse`
`mse_model`
`mse_resid`	Mean squared error of the residuals.
`mse_total`
`nobs`	Number of observations n.
`prsquared`
`pvalues`	The two-tailed p values for the t-stats of the params.
`resid`	The residuals of the model.
`resid_pearson`	Residuals, normalized to have unit variance.
`rsquared`
`rsquared_adj`
`ssr`	Sum of squared (whitened) residuals.
`tvalues`	Return the t-statistic for a given parameter estimate.
`uncentered_tss`
`use_t`	Flag indicating to use the Student’s distribution in inference.
`wresid`	The residuals of the transformed/whitened regressand and regressor(s).