statsmodels.discrete.discrete_model.NegativeBinomialP.predict

NegativeBinomialP.predict(params, exog=None, exposure=None, offset=None, which='mean', y_values=None)[source]

Predict response variable of a model given exogenous variables.

Parameters
paramsarray_like

2d array of fitted parameters of the model. Should be in the order returned from the model.

exogarray_like, optional

1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only have one regressor and would like to do prediction, you must provide a 2d array with shape[1] == 1.

offsetarray_like, optional

Offset is added to the linear prediction with coefficient equal to 1.

exposurearray_like, optional

Log(exposure) is added to the linear prediction with coefficient

equal to 1.
which‘mean’, ‘linear’, ‘prob’, optional.

‘mean’ returns the exp of linear predictor exp(dot(exog,params)). ‘linear’ returns the linear predictor dot(exog,params). ‘prob’ return probabilities for counts from 0 to max(endog). Default is ‘mean’.