run_pattern {missingHE}

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

Description

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

Usage

run_pattern(type, dist_e, dist_c, inits, d_list, d1, d2, restriction, ppc)

Arguments

type

Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff), Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR).

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.

d_list

a list of the number and types of patterns in the data.

d1

Patterns in the control.

d2

Patterns in the intervention.

restriction

type of identifying restriction to be imposed.

ppc

Logical. If ppc is TRUE, the estimates of the parameters that can be used to generate replications from the model are saved.

Examples

#Internal function only
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