statsmodels.discrete.discrete_model.Probit.predict Probit.predict(params, exog=None, linear=False) Predict response variable of a model given exogenous variables. Parameters: params : array-like Fitted parameters of the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Ret

statsmodels.discrete.discrete_model.Probit.loglikeobs Probit.loglikeobs(params) [source] Log-likelihood of probit model for each observation Parameters: params : array-like The parameters of the model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.Probit.jac Probit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Probit model Jacobian for each observation Parameters: params : array-like The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations Where . This simplification comes from the fact that the normal distribution

statsmodels.discrete.discrete_model.Probit.loglike Probit.loglike(params) [source] Log-likelihood of probit model (i.e., the normal distribution). Parameters: params : array-like The parameters of the model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.Probit.initialize Probit.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.genmod.families.links.probit.inverse_deriv probit.inverse_deriv(z) Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.genmod.families.links.probit.inverse probit.inverse(z) The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.discrete.discrete_model.Probit.from_formula classmethod Probit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : e

statsmodels.discrete.discrete_model.Probit.information Probit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.Probit.hessian Probit.hessian(params) [source] Probit model Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes where and