Sets default priors for JAGS model code

This function creates JAGS code snippets for default MBNMA model priors.

default.priors(
  fun = dloglin(),
  UME = FALSE,
  regress.mat = NULL,
  regress.effect = "common",
  om = list(rel = 5, abs = 10)
)

Arguments

fun

An object of class("dosefun") that specifies a functional form to be assigned to the dose-response. See Details.

UME

A boolean object to indicate whether to fit an Unrelated Mean Effects model that does not assume consistency and so can be used to test if the consistency assumption is valid.

regress.mat

A Nstudy x Ncovariate design matrix of meta-regression covariates

regress.effect

Indicates whether effect modification should be assumed to be "common" (assumed to be equal versus Placebo throughout the network), "random" (assumed to be exchangeable versus Placebo throughout the network), "agent" (assumed to be equal versus Placebo within each agent), or "class" (assumed to be equal versus Placebo within each class).

om

a list with two elements that report the maximum relative ("rel") and maximum absolute ("abs") efficacies on the link scale.

Value

A list, each element of which is a named JAGS snippet corresponding to a prior in the MBNMA JAGS code.

Examples

# \donttest{
default.priors(fun=demax())
#> $rho
#> [1] "rho ~ dunif(0,1)"
#> 
#> $mu
#> [1] "mu[i] ~ dnorm(0,0.0001)"
#> 
#> $direct
#> [1] "direct ~ dnorm(0,0.0001)"
#> 
#> $sd
#> [1] "sd ~ dunif(0, 5)"
#> 
#> $emax
#> [1] "emax[k] ~ dnorm(0,0.0001)"
#> 
#> $EMAX
#> [1] "EMAX[k] ~ dnorm(0,0.0001)"
#> 
#> $sd.emax
#> [1] "sd.emax ~ dunif(0, 5)"
#> 
#> $sd.EMAX
#> [1] "sd.EMAX ~ dunif(0, 5)"
#> 
#> $ed50
#> [1] "ed50[k] ~ dnorm(0,0.0001) T(0,)"
#> 
#> $ED50
#> [1] "ED50[k] ~ dnorm(0,0.0001) T(0,)"
#> 
#> $sd.ed50
#> [1] "sd.ed50 ~ dunif(0, 5)"
#> 
#> $sd.ED50
#> [1] "sd.ED50 ~ dunif(0, 5)"
#> 
#> $d
#> [1] "d[k] ~ dnorm(0,0.0001)"
#> 
# }