dclone (version 1.2-0)

bugs.fit: 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, 
format = c("mcmc.list", "bugs"), program = c("winbugs", "openbugs"), ...)
## S3 method for class 'bugs':
as.mcmc.list(x, ...)

Arguments

data

A list 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 or custommodel object (see Examples).

inits

Optional specification of initial values in the form of a list or a function (see Initialization at jags.model). If NULL, initial values will be generated automatically. It is an erro

format

Required output format.

program

The program to use, not case sensitive. winbugs calls the function bugs, openbugs calls the function openbugs and

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, printing 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.

encoding

UTF-8

See Also

Underlying functions: openbugs, bugs Methods: dcsd, confint.mcmc.list.dc, coef.mcmc.list, quantile.mcmc.list, vcov.mcmc.list.dc

Examples

## fitting with WinBUGS, bugs example
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")
sim <- bugs.fit(dat, param, bugs.model, inits)
dat2 <- dclone(dat, 2, multiply="J")
sim2 <- bugs.fit(dat2, param, bugs.model)
## fitting the model with OpenBUGS
sim3 <- bugs.fit(dat2, param, bugs.model, program="openbugs", n.thin=1)
## fitting the model with JAGS
sim4 <- jags.fit(dat2, param, bugs.model)