bcov_scaled () | |
bcov_unscaled () | |
bse () | |
chisq () | |
conf_int ([alpha, cols, method]) | Returns the confidence interval of the fitted parameters. |
cov_params ([r_matrix, column, scale, cov_p, …]) | Returns the variance/covariance matrix. |
f_test (r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
fittedvalues () | |
initialize (model, params, **kwd) | |
llf () | |
load (fname) | load a pickle, (class method) |
normalized_cov_params () | |
predict ([exog, transform]) | Call self.model.predict with self.params as the first argument. |
pvalues () | |
remove_data () | remove data arrays, all nobs arrays from result and model |
resid () | |
save (fname[, remove_data]) | save a pickle of this instance |
sresid () | |
summary ([yname, xname, title, alpha, return_fmt]) | This is for testing the new summary setup |
summary2 ([xname, yname, title, alpha, …]) | Experimental summary function for 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. |
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 |
weights () | |