statsmodels.discrete.discrete_model.Probit¶

class statsmodels.discrete.discrete_model.Probit(endog, exog, offset=None, check_rank=True, **kwargs)[source]¶

Probit Model

Parameters:¶

endogarray_like

A 1-d endogenous response variable. The dependent variable.

exogarray_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.

offsetarray_like

Offset is added to the linear prediction with coefficient equal to 1.

missingstr

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’.

check_rankbool

Check exog rank to determine model degrees of freedom. Default is True. Setting to False reduces model initialization time when exog.shape[1] is large.

Attributes:¶

endogndarray

A reference to the endogenous response variable

exogndarray

A reference to the exogenous design.

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_constrained(constraints[, start_params])	fit_constraint that returns a results instance
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.
get_distribution(params[, exog, offset])	Get frozen instance of distribution based on predicted parameters.
hessian(params)	Probit model Hessian matrix of the log-likelihood
hessian_factor(params)	Probit model Hessian factor 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, which, linear, offset])	Predict response variable of a model given exogenous variables.
score(params)	Probit model score (gradient) vector
score_factor(params)	Probit model Jacobian for each observation
score_obs(params)	Probit model Jacobian for each observation

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.
link