statsmodels.discrete.discrete_model.Probit

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

Binary choice Probit model

Parameters
  • 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

A reference to the endogenous response variable

Type

array

exog

A reference to the exogenous design.

Type

array

Methods

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, drop_cols])

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.

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

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables