This function modifies default hyper prior parameter values in the type of hurdle model selected according to the type of structural value mechanism and distributions for the outcomes assumed.
prior_hurdle(
type,
dist_e,
dist_c,
pe_fixed,
pc_fixed,
ze_fixed,
zc_fixed,
model_e_random,
model_c_random,
model_se_random,
model_sc_random,
pe_random,
pc_random,
ze_random,
zc_random,
se,
sc
)
Type of structural value mechanism assumed. Choices are Structural Completely At Random (SCAR), and Structural At Random (SAR). 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
Number of fixed effects or the structural indicators model for the effectiveness
Number of fixed effects or the structural indicators model for the costs
Random effects formula for the effectiveness model
Random effects formula for the costs model
Random effects formula for the structural indicators model for the effectiveness
Random effects formula for the structural indicators model for the costs
Number of random effects for the effectiveness model
Number of random effects for the cost model
Number of random effects or the structural indicators model for the effectiveness
Number of random effects or the structural indicators model for the costs
Structural value for the effectiveness
Structural value for the costs
#Internal function only
#no examples
#
#
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