# JAGS

##### Markov Chain Monte Carlo for BAMLSS using JAGS

This sampler function for BAMLSS is an interface to the JAGS library
using package `rjags`

. The function basically interprets the
`bamlss.frame`

into BUGS code, similar to the `jagam`

function of
package `mgcv`

. I.e., the function uses the random effects representation of
smooth terms, see the transformer function `randomize`

to generate the BUGS code.

Note that estimating BAMLSS with JAGS is not very efficient.
Also note that this function is more experimental and support is only provided for a small
number of `bamlss.family`

objects.

Function `BUGSeta()`

therefore computes the code and data for one parameter of
the modeled distribution. Function `BUGSmodel()`

then collects all parameter model code
and data, which can be send to JAGS.

- Keywords
- regression

##### Usage

```
## Sampler function:
JAGS(x, y, family, start = NULL,
tdir = NULL, n.chains = 1, n.adapt = 100,
n.iter = 4000, thin = 2, burnin = 1000,
seed = NULL, verbose = TRUE, set.inits = TRUE,
save.all = FALSE, modules = NULL, ...)
```## Function to interpret an additive predictor into BUGS code:
BUGSeta(x, id = NULL, ...)

## Function to interpret the full BAMLSS:
BUGSmodel(x, family, is.stan = FALSE, reference = NULL, ...)

##### Arguments

- x
For function

`JAGS()`

and`BUGSmodel()`

the`x`

list, as returned from function`bamlss.frame`

, holding all model matrices and other information that is used for fitting the model. For function`BUGSeta()`

argument`x`

is one element of the`x`

object, i.e., one parameter.- y
The model response, as returned from function

`bamlss.frame`

.- family
A bamlss family object, see

`family.bamlss`

.- start
A named numeric vector containing possible starting values, the names are based on function

`parameters`

.- tdir
The path to the temporary directory that should be used.

- n.chains
Specifies the number of sequential MCMC chains that should be run with JAGS.

- n.adapt
Specifies the number of iterations that should be used as an initial adaptive phase.

- n.iter
Sets the number of MCMC iterations.

- thin
Defines the thinning parameter for MCMC simulation. E.g.,

`thin = 10`

means, that only every 10th sampled parameter will be stored.- burnin
Sets the burn-in phase of the sampler, i.e., the number of starting samples that should be removed.

- seed
Sets the seed.

- verbose
Print information during runtime of the algorithm.

- set.inits
Should initial values of BAMLSS

`parameters`

be provided to JAGS, if available. Set in argument`start`

.- save.all
Should all JAGS files be saved in

`tdir`

.- modules
Specify additional modules that should be loaded, see function

`load.module`

.- id
Character, the current parameter name for which the BUGS code should be produced.

- is.stan
Should the BUGS code be translated to STAN code. Note that this is only experimental.

- reference
A

`character`

specifying a reference category, e.g., when fitting a multinomial model.- …
Currently not used.

##### Value

Function `JAGS()`

returns samples of parameters. The samples are provided as a `mcmc`

matrix. If `n.chains > 1`

, the samples are provided as a `mcmc.list`

.

Function `BUGSeta()`

returns the BUGS model code and preprocessed data for one
additive predictor. Function `BUGSmodel()`

then combines all single BUGS code chunks and
the data and creates the final BUGS model code that can be send to JAGS.

##### Note

Note that for setting up a new family object to be used with `JAGS()`

additional
information needs to be supplied. The extra information must be placed within the
family object in an element named `"bugs"`

. The following entries should be supplied
within the `..$bugs`

list:

`"dist"`

. The name of the distribution in BUGS/JAGS model language.`"eta"`

. The function that computes the BUGS code for one structured additive predictor. Function`BUGSeta()`

is used per default.`"model"`

. The function that merges all single predictor BUGS model code and data. The default function is`BUGSmodel()`

.`"reparam"`

. A named vector of character strings that specify a re-parametrization.

See also the example code of `family.bamlss`

.

##### See Also

`bamlss`

, `bamlss.frame`

,
`bamlss.engine.setup`

, `set.starting.values`

, `bfit`

,
`GMCMC`

##### Examples

```
# NOT RUN {
## Simulated data example illustrating
## how to call the sampler function.
## This is done internally within
## the setup of function bamlss().
d <- GAMart()
f <- num ~ s(x1, bs = "ps")
bf <- bamlss.frame(f, data = d, family = "gaussian")
## First, find starting values with optimizer.
opt <- with(bf, bfit(x, y, family))
## Sample with JAGS.
if(require("rjags")) {
samps <- with(bf, JAGS(x, y, family, start = opt$parameters))
plot(samps)
}
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*