sam_JAGS | R Documentation |
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.
## 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, ...)
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 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 |
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. |
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 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 ..$bugs
list:
"dist"
. The name of the distribution in BUGS/JAGS model language.
"eta"
. The function that computes the BUGS code for one structured additive predictor.
Function BUGSeta()
is used per default.
"model"
. The function that merges all single predictor BUGS model code and data.
The default function is BUGSmodel()
.
"reparam"
. A named vector of character strings that specify a re-parametrization.
See also the example code of family.bamlss
.
bamlss
, bamlss.frame
,
bamlss.engine.setup
, set.starting.values
, bfit
,
GMCMC
## Not run: ## Simulated data example illustrating ## how to call the sampler function. ## This is done internally within ## the setup of function bamlss(). d <- GAMart() f <- num ~ s(x1, bs = "ps") bf <- bamlss.frame(f, data = d, family = "gaussian") ## First, find starting values with optimizer. opt <- with(bf, opt_bfit(x, y, family)) ## Sample with JAGS. if(require("rjags")) { samps <- with(bf, sam_JAGS(x, y, family, start = opt$parameters)) plot(samps) b <- bamlss(f, data = d, family = "gaussian", sampler = sam_JAGS) plot(b) } ## End(Not run)
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