statsmodels.discrete.truncated_model.HurdleCountModel.predict

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

Predict response variable 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)

  • ‘mean-main’ : mean parameter of truncated count model. Note, this is not the mean of the truncated distribution.

  • ‘linear’ : the linear predictor of the truncated count model.

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

  • ‘prob-main’ : probability of selecting the main model which is the probability of observing a nonzero count P(y > 0 | x).

  • ‘prob-zero’ : probability of observing a zero count. P(y=0 | x). This is equal to is 1 - prob-main

  • ‘prob-trunc’ : probability of truncation of the truncated count model. This is the probability of observing a zero count implied by the truncation model.

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

  • ‘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"

Returns:
predicted values

Notes

‘prob-zero’ / ‘prob-trunc’ is the ratio of probabilities of observing a zero count between hurdle model and the truncated count model. If this ratio is larger than one, then the hurdle model has an inflated number of zeros compared to the count model. If it is smaller than one, then the number of zeros is deflated.

Last update: Dec 23, 2024