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
- Attributes
- 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.
Calculated as ratio of largest to smallest eigenvalue.
- cov_HC0
Heteroscedasticity robust covariance matrix. See HC0_se.
- cov_HC1
Heteroscedasticity robust covariance matrix. See HC1_se.
- cov_HC2
Heteroscedasticity robust covariance matrix. See HC2_se.
- cov_HC3
Heteroscedasticity robust covariance matrix. See HC3_se.
- eigenvals
Return eigenvalues sorted in decreasing order.
- ess
The explained sum of squares.
If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.
- 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.
Calculated as the mean squared error of the model divided by the mean squared error of the residuals if the nonrobust covariance is used. Otherwise computed using a Wald-like quadratic form that tests whether all coefficients (excluding the constant) are zero.
- llf
- mse
- mse_model
- mse_resid
Mean squared error of the residuals.
The sum of squared residuals divided by the residual degrees of freedom.
- 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.
- array_like
The array wresid normalized by the sqrt of the scale 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_tFlag indicating to use the Student’s distribution in inference.
- wresid
The residuals of the transformed/whitened regressand and regressor(s).
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
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument.
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.
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
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument.
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
The standard errors of the parameter estimates.
Return condition number of exogenous matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Return eigenvalues sorted in decreasing order.
The explained sum of squares.
The p-value of the F-statistic.
The predicted values for the original (unwhitened) design.
F-statistic of the fully specified model.
Mean squared error of the residuals.
Number of observations n.
The two-tailed p values for the t-stats of the params.
The residuals of the model.
Residuals, normalized to have unit variance.
Sum of squared (whitened) residuals.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.
The residuals of the transformed/whitened regressand and regressor(s).