statsmodels.discrete.discrete_model.ProbitResults

class statsmodels.discrete.discrete_model.ProbitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)

A results class for Probit 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()
fittedvalues()
get_margeff([at, method, atexog, dummy, count])	Get marginal effects of the fitted model.
llnull()
llr()
llr_pvalue()
pred_table([threshold])	Prediction table
prsquared()
resid_dev()	Deviance residuals
resid_generalized()	Generalized residuals
resid_pearson()	Pearson residuals
resid_response()	The response residuals
summary([yname, xname, title, alpha, yname_list])	Summarize the Regression Results
summary2([yname, xname, title, alpha, ...])	Experimental function to summarize regression results

Attributes

use_t