scikits.statsmodels.discretemod.Probit

class scikits.statsmodels.discretemod.Probit(endog, exog=None)

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.
nobs float The number of observations of the model.

Methods

cdf(X) Probit (Normal) cumulative distribution function
fit([start_params, maxiter, method, tol]) Fits the binary probit model.
hessian(params) Probit model Hessian matrix of the log-likelihood
information(params) Fisher information matrix of model
initialize() Initialize is called by
loglike(params) Log-likelihood of probit model (i.e., the normal distribution).
pdf(X) Probit (Normal) probability density function
predict(design) After a model has been fit predict returns the fitted values.
score(params) Probit model score (gradient) vector

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