sam_JAGS {bamlss}  R Documentation 
Markov Chain Monte Carlo for BAMLSS using JAGS
Description
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.
Usage
## Sampler functions:
sam_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, ...)
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 
y 
The model response, as returned from function 
family 
A bamlss family object, see 
start 
A named numeric vector containing possible starting values, the names are based on
function 
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., 
burnin 
Sets the burnin 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 
save.all 
Should all JAGS files be saved in 
modules 
Specify additional modules that should be loaded, see function 
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 
... 
Currently not used. 
Value
Function sam_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 sam_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. FunctionBUGSeta()
is used per default. 
"model"
. The function that merges all single predictor BUGS model code and data. The default function isBUGSmodel()
. 
"reparam"
. A named vector of character strings that specify a reparametrization.
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, opt_bfit(x, y, family))
## Sample with JAGS.
if(require("rjags")) {
samps < with(bf, sam_JAGS(x, y, family, start = opt$parameters))
plot(samps)
b < bamlss(f, data = d, family = "gaussian", sampler = sam_JAGS)
plot(b)
}
## End(Not run)