run_hurdle: An internal function to execute a JAGS hurdle model and get...

In missingHE: Missing Outcome Data in Health Economic Evaluation

An internal function to execute a JAGS hurdle model and get posterior results

Description

This function fits a JAGS using the jags function and obtain posterior inferences.

Usage

run_hurdle(data_model, type, dist_e, dist_c, model_info)

Arguments

data_model	list containing the data for the model to be passed to JAGS.
type	Type of structural value mechanism assumed. Choices are Structural Completely At Random (SCAR), Structural At Random (SAR),
dist_e	distribution assumed for the effects. Current available choices are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'), Weibull ('weib'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('negbin') or Bernoulli ('bern').
dist_c	Distribution assumed for the costs. Current available choices are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm').
model_info	list containing model and MCMC information to be passed to JAGS.

Examples

#Internal function only
#No examples
#
#

missingHE documentation built on March 19, 2026, 5:06 p.m.