prior_pattern: An internal function to change the hyperprior parameters in...

Description Usage Arguments Examples

View source: R/prior_pattern.R

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

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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

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#Internal function only
#no examples
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missingHE documentation built on July 1, 2020, 5:50 p.m.