statsmodels.discrete.discrete_model.PoissonResults.predict_prob#
- PoissonResults.predict_prob(n=None, exog=None, exposure=None, offset=None, transform=True)[source]#
Return predicted probability of each count level for each observation
- Parameters:
- narray_like or
int The counts for which you want the probabilities. If n is None then the probabilities for each count from 0 to max(y) are given.
- exogarray_like,
optional Design / exogenous data. If exog is None, model exog is used.
- exposurearray_like,
optional Log(exposure) is added to the linear prediction with coefficient equal to 1. If exposure is not provided and exog is None, uses the model’s exposure if present.
- offsetarray_like,
optional Offset is added to the linear prediction with coefficient equal to 1. If offset is not provided and exog is None, uses the model’s offset if present.
- transformbool,
optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True.
- narray_like or
- Returns:
ndarrayA nobs x n array where len(n) columns are indexed by the count n. If n is None, then column 0 is the probability that each observation is 0, column 1 is the probability that each observation is 1, etc.