statsmodels.discrete.discrete_model.ProbitResults.load classmethod ProbitResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.discrete.discrete_model.ProbitResults.initialize ProbitResults.initialize(model, params, **kwd)

statsmodels.discrete.discrete_model.ProbitResults.normalized_cov_params ProbitResults.normalized_cov_params()

statsmodels.discrete.discrete_model.ProbitResults.f_test ProbitResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test

statsmodels.discrete.discrete_model.ProbitResults.get_margeff ProbitResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?al

statsmodels.discrete.discrete_model.ProbitResults.conf_int ProbitResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interva

statsmodels.discrete.discrete_model.ProbitResults.cov_params ProbitResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like

statsmodels.discrete.discrete_model.Probit.score_obs Probit.score_obs(params) [source] 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 is symmetric.

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.score Probit.score(params) [source] Probit model score (gradient) vector Parameters: params : array-like The parameters of the model Returns: score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params Notes Where . This simplification comes from the fact that the normal distribution is symmetric.