# Get current priors from JAGS model code

`get.prior.Rd`

Identical to `get.prior()`

in MBNMAdose.
This function takes JAGS model presented as a string and identifies what
prior values have been used for calculation.

## 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 initialisation.

## Examples

```
# \donttest{
# Create mb.network object using an MBNMAtime dataset
network <- mb.network(osteopain)
#> Reference treatment is `Pl_0`
#> Studies reporting change from baseline automatically identified from the data
# Create mb.network object using an MBNMAdose dataset
# Run linear MBNMA
result <- mb.run(network, fun=tpoly(degree=1,
pool.1="rel", method.1="random"))
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 417
#> Unobserved stochastic nodes: 163
#> Total graph size: 7701
#>
#> Initializing model
#>
# Obtain model prior values
get.prior(result$model.arg$jagscode)
#> $mu.1
#> [1] "dnorm(0,0.0001)"
#>
#> $alpha
#> [1] "dnorm(0,0.0001)"
#>
#> $d.1
#> [1] "dnorm(0,0.001)"
#>
#> $sd.beta.1
#> [1] "dnorm(0,0.05) T(0,)"
#>
# ...also equivalent to
print(result$model.arg$priors)
#> $mu.1
#> [1] "dnorm(0,0.0001)"
#>
#> $alpha
#> [1] "dnorm(0,0.0001)"
#>
#> $d.1
#> [1] "dnorm(0,0.001)"
#>
#> $sd.beta.1
#> [1] "dnorm(0,0.05) T(0,)"
#>
# }
```