statsmodels.discrete.discrete_model.NegativeBinomialResults.remove_data NegativeBinomialResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the f

statsmodels.discrete.discrete_model.NegativeBinomialResults.predict NegativeBinomialResults.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

statsmodels.discrete.discrete_model.NegativeBinomialResults.normalized_cov_params NegativeBinomialResults.normalized_cov_params()

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

statsmodels.discrete.discrete_model.NegativeBinomialResults.get_margeff NegativeBinomialResults.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

statsmodels.discrete.discrete_model.NegativeBinomialResults.f_test NegativeBinomialResults.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

statsmodels.discrete.discrete_model.NegativeBinomialResults.cov_params NegativeBinomialResults.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.

statsmodels.discrete.discrete_model.NegativeBinomialResults.conf_int NegativeBinomialResults.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 th

statsmodels.genmod.families.family.NegativeBinomial.weights NegativeBinomial.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))