statsmodels.discrete.discrete_model.NegativeBinomialP.predict

NegativeBinomialP.predict(params, exog=None, exposure=None, offset=None, which='mean')

Predict response variable of a model given exogenous variables.

Parameters:

• params (array-like) – 2d array of fitted parameters of the model. Should be in the order returned from the model.
• exog (array-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.
• linear (bool, optional) – If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor.
• offset (array_like, optional) – Offset is added to the linear prediction with coefficient equal to 1.
• exposure (array_like, optional) – Log(exposure) is added to the linear prediction with coefficient
• to 1. (equal) –
• 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’.

Notes