statsmodels.discrete.discrete_model.DiscreteResults.predict DiscreteResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a d

statsmodels.discrete.discrete_model.DiscreteResults.normalized_cov_params DiscreteResults.normalized_cov_params()

statsmodels.discrete.discrete_model.DiscreteResults.load classmethod DiscreteResults.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.DiscreteResults.initialize DiscreteResults.initialize(model, params, **kwd)

statsmodels.discrete.discrete_model.DiscreteResults.get_margeff DiscreteResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) [source] 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 r

statsmodels.discrete.discrete_model.DiscreteResults.f_test DiscreteResults.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 t

statsmodels.discrete.discrete_model.DiscreteResults.cov_params DiscreteResults.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-

statsmodels.discrete.discrete_model.DiscreteResults.conf_int DiscreteResults.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_int

statsmodels.discrete.discrete_model.DiscreteModel.score DiscreteModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.DiscreteModel.predict DiscreteModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables.