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statsmodels.discrete.discrete_model.Probit

class statsmodels.discrete.discrete_model.Probit(endog, exog)[source]

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
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

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