statsmodels.discrete.discrete_model.CountResults.cov_params CountResults.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.CountResults.conf_int CountResults.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_interval.

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

statsmodels.discrete.discrete_model.CountModel.predict CountModel.predict(params, exog=None, exposure=None, offset=None, linear=False) [source] Predict response variable of a count model given exogenous variables. Notes If exposure is specified, then it will be logged by the method. The user does not need to log it first.

statsmodels.discrete.discrete_model.CountModel.pdf CountModel.pdf(X) The probability density (mass) function of the model.

statsmodels.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

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

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

statsmodels.discrete.discrete_model.CountModel.hessian CountModel.hessian(params) The Hessian matrix of the model

statsmodels.discrete.discrete_model.CountModel.from_formula classmethod CountModel.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