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

Description

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

Usage

run_hurdle(type, dist_e, dist_c, inits, se, sc, sde, sdc, ppc)

Arguments

type: Type of structural value mechanism assumed. Choices are Structural Completely At Random (SCAR), and Structural At Random (SAR).
dist_e: distribution assumed for the effects. Current available chocies are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'), Weibull ('weibull'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('nbinom') or Bernoulli ('bern')
dist_c: distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm').
inits: a list with elements equal to the number of chains selected; each element of the list is itself a list of starting values for the BUGS model, or a function creating (possibly random) initial values. If inits is NULL, JAGS will generate initial values for parameters
se: Structural value to be found in the effect data. If set to NULL, no structural value is chosen and a standard model for the effects is run.
sc: Structural value to be found in the cost data. If set to NULL, no structural value is chosen and a standard model for the costs is run.
sde: hyper-prior value for the standard deviation of the distribution of the structural effects. The default value is 1.0E-6 to approximate a point mass at the structural value provided by the user.
sdc: hyper-prior value for the standard deviation of the distribution of the structural costs. The default value is 1.0E-6 to approximate a point mass at the structural value provided by the user.
ppc: Logical. If ppc is TRUE, the estimates of the parameters that can be used to generate replications from the model are saved.

Examples

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