discrete.discrete_model.LogitResults()

statsmodels.discrete.discrete_model.LogitResults

class statsmodels.discrete.discrete_model.LogitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source]

A results class for Logit Model

Parameters:

model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix.

Returns:

*Attributes* : aic : float Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept. bic : float Bayesian information criterion. -2*llf + ln(nobs)*p where p is the number of regressors including the intercept. bse : array The standard errors of the coefficients. df_resid : float See model definition. df_model : float See model definition. fitted_values : array Linear predictor XB. llf : float Value of the loglikelihood llnull : float Value of the constant-only loglikelihood llr : float Likelihood ratio chi-squared statistic; -2*(llnull - llf) llr_pvalue : float The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom df_model. prsquared : float McFadden?s pseudo-R-squared. 1 - (llf / llnull)

Methods

aic()
bic()
bse()
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()
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)
normalized_cov_params()
pred_table([threshold])	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_dev()	Deviance residuals
resid_generalized()	Generalized residuals
resid_pearson()	Pearson residuals
resid_response()	The response residuals
save(fname[, remove_data])	save a pickle of this instance
summary([yname, xname, title, alpha, yname_list])	Summarize the Regression Results
summary2([yname, xname, title, alpha, ...])	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