scikits.statsmodels.discretemod.Logit

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

Binary choice logit 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) The logistic cumulative distribution function
fit([start_params, maxiter, method, tol]) Fits the binary logit model.
hessian(params) Logit model Hessian matrix of the log-likelihood
information(params) Fisher information matrix of model
initialize() Initialize is called by
loglike(params) Log-likelihood of logit model.
pdf(X) The logistic probability density function
predict(design) After a model has been fit predict returns the fitted values.
score(params) Logit model score (gradient) vector of the log-likelihood

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