statsmodels.discrete.truncated_model.HurdleCountModel¶

class statsmodels.discrete.truncated_model.HurdleCountModel(endog, exog, offset=None, dist='poisson', zerodist='poisson', p=2, pzero=2, exposure=None, missing='none', **kwargs)[source]¶

Hurdle model for count data

Added in version 0.14.0.

Parameters:¶

endogarray_like

A 1-d endogenous response variable. The dependent variable.

exogarray_like

A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant.

offsetarray_like

Offset is added to the linear prediction with coefficient equal to 1.

exposurearray_like

Log(exposure) is added to the linear prediction with coefficient equal to 1.

Attributes:¶

endogarray

A reference to the endogenous response variable

exogarray

A reference to the exogenous design.

diststr

Log-likelihood type of count model family. ‘poisson’ or ‘negbin’

zerodiststr

Log-likelihood type of zero hurdle model family. ‘poisson’, ‘negbin’

pscalar

Define parameterization for count model. Used when dist=’negbin’.

pzeroscalar

Define parameterization parameter zero hurdle model family. Used when zerodist=’negbin’.

Notes

The parameters in the NegativeBinomial zero model are not identified if the predicted mean is constant. If there is no or only little variation in the predicted mean, then convergence might fail, hessian might not be invertible or parameter estimates will have large standard errors.

References

not yet

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

Methods

cdf(X)	The cumulative distribution function of the model.
cov_params_func_l1(likelihood_model, xopt, ...)	Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.
fit([start_params, method, maxiter, ...])	Fit the model using maximum likelihood.
fit_regularized([start_params, method, ...])	Fit the model using a regularized maximum likelihood.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	The Hessian matrix of the model.
information(params)	Fisher information matrix of model.
initialize()	Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
loglike(params)	Loglikelihood of Generic Hurdle model
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	Predict response variable or other statistic given exogenous variables.
score(params)	Score vector of model.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.