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)

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, optional

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, optional

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

dist{‘poisson’, ‘negbin’}, optional

Log-likelihood type of count model family. Default is ‘poisson’.

zerodist{‘poisson’, ‘negbin’}, optional

Log-likelihood type of zero hurdle model family. Default is ‘poisson’.

pint, optional

Dispersion power parameter for the NegativeBinomialP count model. Used when dist=’negbin’. Default is 2.

pzeroint, optional

Dispersion power parameter for the NegativeBinomialP zero hurdle model. Used when zerodist=’negbin’. Default is 2.

exposurearray_like, optional

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

missingstr, optional

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’.

Attributes:

endogndarray

A reference to the endogenous response variable

exogndarray

A reference to the exogenous design.

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)	Hurdle model Hessian matrix of the log-likelihood.
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)	Log-likelihood 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
score_obs(params)	Hurdle model score (gradient) vector of the log-likelihood.

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

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)	Hurdle model Hessian matrix of the log-likelihood.
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)	Log-likelihood 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
score_obs(params)	Hurdle model score (gradient) vector of the log-likelihood.

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables