prior_pattern {missingHE}

An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed

Description

This function modifies default hyper prior parameter values in the type of selection model selected according to the type of missingness mechanism and distributions for the outcomes assumed.

Usage

prior_pattern(
  type,
  dist_e,
  dist_c,
  pe_fixed,
  pc_fixed,
  model_e_random,
  model_c_random,
  pe_random,
  pc_random,
  d_list,
  restriction
)

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). For a complete list of all available hyper parameters and types of models see the manual.

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')

pe_fixed

Number of fixed effects for the effectiveness model

pc_fixed

Number of fixed effects for the cost model

model_e_random

Random effects formula for the effectiveness model

model_c_random

Random effects formula for the costs model

pe_random

Number of random effects for the effectiveness model

pc_random

Number of random effects for the cost model

d_list

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

restriction

type of identifying restriction to be imposed

Examples

#Internal function only
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