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

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)