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
modelA DiscreteModel instance
paramsarray_like

The parameters of a fitted model.

hessianarray_like

The hessian of the fitted model.

scalefloat

A scale parameter for the covariance matrix.

Attributes
df_residfloat

See model definition.

df_modelfloat

See model definition.

llffloat

Value of the loglikelihood

Methods

conf_int([alpha, cols])

Construct confidence interval for the fitted parameters.

cov_params([r_matrix, column, scale, cov_p, ...])

Compute the variance/covariance matrix.

f_test(r_matrix[, cov_p, invcov])

Compute the F-test for a joint linear hypothesis.

get_influence()

Get an instance of MLEInfluence with influence and outlier measures

get_margeff([at, method, atexog, dummy, count])

Get marginal effects of the fitted model.

get_prediction([exog, transform, which, ...])

Compute prediction results when endpoint transformation is valid.

info_criteria(crit[, dk_params])

Return an information criterion for the model.

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

pred_table([threshold])

Prediction table

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.

score_test([exog_extra, params_constrained, ...])

score test for restrictions or for omitted variables

set_null_options([llnull, attach_results])

Set the fit options for the Null (constant-only) model.

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, 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, invcov, use_f, ...])

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

aic

Akaike information criterion.

bic

Bayesian information criterion.

bse

The standard errors of the parameter estimates.

fittedvalues

Linear predictor XB.

im_ratio

llf

Log-likelihood of model

llnull

Value of the constant-only loglikelihood

llr

Likelihood ratio chi-squared statistic; -2*(llnull - llf)

llr_pvalue

The chi-squared probability of getting a log-likelihood ratio statistic greater than llr.

prsquared

McFadden's pseudo-R-squared.

pvalues

The two-tailed p values for the t-stats of the params.

resid_dev

Deviance residuals

resid_generalized

Generalized residuals

resid_pearson

Pearson residuals

resid_response

The response residuals

tvalues

Return the t-statistic for a given parameter estimate.

use_t

Flag indicating to use the Student's distribution in inference.