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.
prior_pattern(
type,
dist_e,
dist_c,
pe_fixed,
pc_fixed,
model_e_random,
model_c_random,
pe_random,
pc_random,
d_list,
restriction
)
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.
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')
Distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm')
Number of fixed effects for the effectiveness model
Number of fixed effects for the cost model
Random effects formula for the effectiveness model
Random effects formula for the costs model
Number of random effects for the effectiveness model
Number of random effects for the cost model
a list of the number and types of patterns in the data
type of identifying restriction to be imposed
#Internal function only
#no examples
#
#
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