A results class for multinomial data
Parameters: | model : A DiscreteModel instance params : array-like
hessian : array-like
scale : float
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Returns: | *Attributes* : aic : float
bic : float
bse : array
df_resid : float
df_model : float
fitted_values : array
llf : float
llnull : float
llr : float
llr_pvalue : float
prsquared : float
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Methods
aic() | |
bic() | |
bse() | |
conf_int([alpha, cols]) | |
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() | |
get_margeff([at, method, atexog, dummy, count]) | Get marginal effects of the fitted model. |
initialize(model, params, **kwd) | |
llf() | |
llnull() | |
llr() | |
llr_pvalue() | |
load(fname) | load a pickle, (class method) |
margeff() | |
normalized_cov_params() | |
pred_table() | Returns the J x J prediction table. |
predict([exog, transform]) | Call self.model.predict with self.params as the first argument. |
prsquared() | |
pvalues() | |
remove_data() | remove data arrays, all nobs arrays from result and model |
resid_misclassified() | Residuals indicating which observations are misclassified. |
save(fname[, remove_data]) | save a pickle of this instance |
summary([yname, xname, title, alpha, yname_list]) | Summarize the Regression Results |
summary2([alpha, float_format]) | Experimental function to summarize 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 |
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. |
Attributes
use_t | bool(x) -> bool |