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

use_t Flag indicating to use the Student’s distribution in inference.

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.

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).