Get current priors from JAGS model code

Identical to get.prior() in MBNMAtime package. This function takes JAGS model presented as a string and identifies what prior values have been used for calculation.

get.prior(model)

Arguments

model

A character object of JAGS MBNMA model code

Value

A character vector, each element of which is a line of JAGS code corresponding to a prior in the JAGS code.

Details

Even if an MBNMA model that has not initialised successfully and results have not been calculated, the JAGS model for it is saved in mbnma$model.arg$jagscode and therefore priors can still be obtained. This allows for priors to be changed even in failing models, which may help solve issues with compiling or updating.

Examples

# \donttest{
# Using the triptans data
network <- mbnma.network(triptans)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering

# Run an Emax dose-response MBNMA
result <- mbnma.run(network, fun=demax(), method="random")
#> `likelihood` not given by user - set to `binomial` based on data provided
#> `link` not given by user - set to `logit` based on assigned value for `likelihood`
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 197
#>    Total graph size: 4115
#> 
#> Initializing model
#> 

# Obtain model prior values
print(result$model.arg$priors)
#> $mu
#> [1] "dnorm(0,0.0001)"
#> 
#> $ed50
#> [1] "dnorm(0,0.0001) T(0,)"
#> 
#> $emax
#> [1] "dnorm(0,0.0001)"
#> 
#> $sd
#> [1] "dunif(0, 6.021)"
#> 

# Priors when using mbnma.run with an exponential function
result <- mbnma.run(network, fun=dexp(), method="random")
#> `likelihood` not given by user - set to `binomial` based on data provided
#> `link` not given by user - set to `logit` based on assigned value for `likelihood`
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 190
#>    Total graph size: 4087
#> 
#> Initializing model
#> 
print(result$model.arg$priors)
#> $mu
#> [1] "dnorm(0,0.0001)"
#> 
#> $emax
#> [1] "dnorm(0,0.0001)"
#> 
#> $sd
#> [1] "dunif(0, 6.021)"
#> 
# }