statsmodels.discrete.count_model.GenericZeroInflated.predict¶

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

Predict expected response or other statistic given exogenous variables.

Parameters:¶

paramsarray_like

The parameters of the model.

exogndarray, optional

Explanatory variables for the main count model. If exog is None, then the data from the model will be used.

exog_inflndarray, optional

Explanatory variables for the zero-inflation model. exog_infl has to be provided if exog was provided unless exog_infl in the model is only a constant.

offsetndarray, optional

Offset is added to the linear predictor of the mean function with coefficient equal to 1. Default is zero if exog is not None, and the model offset if exog is None.

exposurendarray, optional

Log(exposure) is added to the linear predictor with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. Default is one if exog is is not None, and it is the model exposure if exog is None.

whichstr (optional)

Statitistic to predict. Default is ‘mean’.

• ‘mean’ : the conditional expectation of endog E(y | x). This takes inflated zeros into account.

• ‘linear’ : the linear predictor of the mean function.

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

• ‘mean-main’ : mean of the main count model

• ‘prob-main’probability of selecting the main model.

  The probability of zero inflation is 1 - prob-main.

• ‘mean-nonzero’ : expected value conditional on having observation larger than zero, E(y | X, y>0)

• ‘prob-zero’ : probability of observing a zero count. P(y=0 | x)

• ‘prob’ : probabilities of each count from 0 to max(endog), or for y_values if those are provided. This is a multivariate return (2-dim when predicting for several observations).

y_valuesarray_like

Values of the random variable endog at which pmf is evaluated. Only used if which="prob"