write_selection_long {missingHE}

An internal function to select which type of selection model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.

Description

An internal function to select which type of selection model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.

Usage

write_selection_long(
  dist_u,
  dist_c,
  type,
  pu_fixed,
  pc_fixed,
  zu_fixed,
  zc_fixed,
  ind_fixed,
  ind_time_fixed,
  pu_random,
  pc_random,
  zu_random,
  zc_random,
  ind_random,
  model_u_random,
  model_c_random,
  model_mu_random,
  model_mc_random
)

Arguments

dist_u

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')

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)

pu_fixed

Number of fixed effects for the effectiveness model

pc_fixed

Number of fixed effects for the cost model

zu_fixed

Number of fixed effects or the missingness indicators model for the effectiveness

zc_fixed

Number of fixed effects or the missingness indicators model for the costs

ind_fixed

Logical; if TRUE independence between effectiveness and costs at the same time is assumed, else correlation is accounted for

ind_time_fixed

Logical; if TRUE independence between effectiveness and costs over time is assumed, else an AR1 correlation structure is accounted for

pu_random

Number of random effects for the effectiveness model

pc_random

Number of random effects for the cost model

zu_random

Number of random effects or the missingness indicators model for the effectiveness

zc_random

Number of random effects or the missingness indicators model for the costs

ind_random

Logical; if TRUE independence at the level of the random effects between effectiveness and costs is assumed, else correlation is accounted for

model_u_random

Random effects formula for the effectiveness model

model_c_random

Random effects formula for the costs model

model_mu_random

Random effects formula for the missingness indicators model for the effectiveness

model_mc_random

Random effects formula for the missingness indicators model for the costs

Examples

#Internal function only
#No examples
#
#