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 response variable or other statistic given exogenous variables.
- Parameters:
- paramsarray_like
The parameters of the model.
- exog
ndarray
,optional
Explanatory variables for the main count model. If
exog
is None, then the data from the model will be used.- exog_infl
ndarray
,optional
Explanatory variables for the zero-inflation model.
exog_infl
has to be provided ifexog
was provided unlessexog_infl
in the model is only a constant.- offset
ndarray
,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.
- exposure
ndarray
,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.
- which
str
(optional
) Statitistic to predict. Default is ‘mean’.
‘mean’ : the conditional expectation of endog E(y | x), i.e. exp of linear predictor.
‘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"