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scikits.statsmodels.discrete.discrete_model.Probit

class scikits.statsmodels.discrete.discrete_model.Probit(endog, exog=None)[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(**kwargs[, start_params, method, ...]) 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
loglike(params) Log-likelihood of probit model (i.e., the normal distribution).
pdf(X) Probit (Normal) probability density function
predict([exog, linear]) Predict response variable of a model given exogenous variables.
score(params) Probit model score (gradient) vector

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