bugs.fit: Fit BUGS models with cloned data

In datacloning/dclone: Data Cloning and MCMC Tools for Maximum Likelihood Methods

Fit BUGS models with cloned data

Description

Convenient functions designed to work well with cloned data arguments and WinBUGS and OpenBUGS.

Usage

bugs.fit(data, params, model, inits = NULL, n.chains = 3,
    format = c("mcmc.list", "bugs"), 
    program = c("winbugs", "openbugs", "brugs"), 
    seed, ...)
## S3 method for class 'bugs'
as.mcmc.list(x, ...)

Arguments

data	A list (or environment) containing the data.
params	Character vector of parameters to be sampled.
model	Character string (name of the model file), a function containing the model, or a custommodel object (see Examples).
inits	Optional specification of initial values in the form of a list or a function. If NULL, initial values will be generated automatically.
n.chains	number of Markov chains.
format	Required output format.
program	The program to use, not case sensitive. winbugs calls the function bugs from package R2WinBUGS, openbugs calls the function bugs from package R2OpenBUGS (this has changed since dclone version 1.8-1, this is now the preferred OpenBUGS interface). brugs calls the function openbugs from package R2WinBUGS and requires the CRAN package BRugs (this is provided for back compatibility purposes, but gives a warning because it is not the preferred interface to R2OpenBUGS).
seed	Random seed (bugs.seed argument for bugs in package R2WinBUGS or bugs in package R2OpenBUGS, seed argument for openbugs). It takes the corresponding default values (NULL or 1) when missing.
x	A fitted 'bugs' object.
...	Further arguments of the bugs function, except for codaPkg are passed also, most notably the ones to set up burn-in, thin, etc. (see Details).

Value

By default, an mcmc.list object. If data cloning is used via the data argument, summary returns a modified summary containing scaled data cloning standard errors (scaled by sqrt(n.clones)), and R_{hat} values (as returned by gelman.diag).

bugs.fit can return a bugs object if format = "bugs". In this case, summary is not changed, but the number of clones used is attached as attribute and can be retrieved by the function nclones.

The function as.mcmc.list.bugs converts a 'bugs' object into 'mcmc.list' and retrieves data cloning information as well.

Author(s)

Peter Solymos

See Also

Underlying functions: bugs in package R2WinBUGS, openbugs in package R2WinBUGS, bugs in package R2OpenBUGS

Methods: dcsd, confint.mcmc.list.dc, coef.mcmc.list, quantile.mcmc.list, vcov.mcmc.list.dc

Examples

## Not run: 
## fitting with WinBUGS, bugs example
if (require(R2WinBUGS)) {
data(schools)
dat <- list(J = nrow(schools), 
    y = schools$estimate, 
    sigma.y = schools$sd)
bugs.model <- function(){
       for (j in 1:J){
         y[j] ~ dnorm (theta[j], tau.y[j])
         theta[j] ~ dnorm (mu.theta, tau.theta)
         tau.y[j] <- pow(sigma.y[j], -2)
       }
       mu.theta ~ dnorm (0.0, 1.0E-6)
       tau.theta <- pow(sigma.theta, -2)
       sigma.theta ~ dunif (0, 1000)
     }  
inits <- function(){
    list(theta=rnorm(nrow(schools), 0, 100), mu.theta=rnorm(1, 0, 100),
         sigma.theta=runif(1, 0, 100))
}
param <- c("mu.theta", "sigma.theta")
if (.Platform$OS.type == "windows") {
sim <- bugs.fit(dat, param, bugs.model, inits)
summary(sim)
}
dat2 <- dclone(dat, 2, multiply="J")
if (.Platform$OS.type == "windows") {
sim2 <- bugs.fit(dat2, param, bugs.model, 
    program="winbugs", n.iter=2000, n.thin=1)
summary(sim2)
}
}
if (require(BRugs)) {
## fitting the model with OpenBUGS
## using the less preferred BRugs interface
sim3 <- bugs.fit(dat2, param, bugs.model, 
    program="brugs", n.iter=2000, n.thin=1)
summary(sim3)
}
if (require(R2OpenBUGS)) {
## fitting the model with OpenBUGS
## using the preferred R2OpenBUGS interface
sim4 <- bugs.fit(dat2, param, bugs.model, 
    program="openbugs", n.iter=2000, n.thin=1)
summary(sim4)
}
if (require(rjags)) {
## fitting the model with JAGS
sim5 <- jags.fit(dat2, param, bugs.model)
summary(sim5)
}

## End(Not run)

datacloning/dclone documentation built on Sept. 29, 2024, 3:21 p.m.