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

Binary choice Probit model


endog : array-like

1-d endogenous response variable. The dependent variable.

exog : array-like

A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant.

missing : str

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’


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


cdf(X) Probit (Normal) cumulative distribution function
cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.
fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood.
fit_regularized([start_params, method, ...]) Fit the model using a regularized maximum likelihood.
from_formula(formula, data[, subset]) Create a Model from a formula and dataframe.
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(*args, **kwds) jac is deprecated, use score_obs instead!
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
score_obs(params) Probit model Jacobian for each observation



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