statsmodels.discrete.discrete_model.Poisson.predict

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

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, then the 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.

  • offset (array_like, optional) – Offset is added to the linear predictor with coefficient equal to 1. Default is zero if exog is not None, and the model offset if exog is None.

  • exposure (array_like, optional) – Log(exposure) is added to the linear prediction with coefficient equal to 1. Default is one if exog is is not None, and is the model exposure if exog is None.

  • which ('mean', 'linear', 'var', 'prob' (optional)) –

    Statitistic to predict. Default is ‘mean’.

    • ’mean’ returns the conditional expectation of endog E(y | x), i.e. exp of linear predictor.

    • ’linear’ returns the linear predictor of the mean function.

    • ’var’ returns the estimated variance of endog implied by the model.

    • ’prob’ return probabilities for counts from 0 to max(endog) or for y_values if those are provided.

  • linear (bool) –

    The linear` keyword is deprecated and will be removed, use ``which keyword instead. If True, returns the linear predicted values. If False or None, then the statistic specified by which will be returned.

  • y_values (array_like) – Values of the random variable endog at which pmf is evaluated. Only used if which="prob"