statsmodels.discrete.discrete_model.Probit

class statsmodels.discrete.discrete_model.Probit(endog, exog)

Binary choice Probit model

Parameters:

endog : array-like

1-d array of the response variable.

exog : array-like

exog is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data.

Attributes

endog	array	A reference to the endogenous response variable
exog	array	A reference to the exogenous design.

Methods

cdf(X)	Probit (Normal) cumulative distribution function
fit([start_params, method, maxiter, ...])	Fit the model using maximum likelihood.
hessian(params)	Probit model Hessian matrix of the log-likelihood
information(params)	Fisher information matrix of model
initialize()	Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
jac(params)	Probit model Jacobian for each observation
loglike(params)	Log-likelihood of probit model (i.e., the normal distribution).
loglikeobs(params)	Log-likelihood of probit model for each observation
pdf(X)	Probit (Normal) probability density function
predict(params[, exog, linear])	Predict response variable of a model given exogenous variables.
score(params)	Probit model score (gradient) vector

Attributes

endog_names
exog_names