stan.fit: Fit Stan models with cloned data

In datacloning/dcextras: Hierarchical Models Made Easy with Data Cloning

Description

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

Usage

stan.fit(data, params, model, inits = NULL,
    seed = sample.int(.Machine$integer.max, 1),
    n.chains = 3,
    format = c("mcmc.list", "stanfit"),
    stan.model = TRUE, fit = NA, chain_id, ...)

stan.parfit(cl, data, params, model, inits = NULL,
    seed = sample.int(.Machine$integer.max, 1),
    n.chains = 3,
    format = c("mcmc.list", "stanfit"),
    stan.model = TRUE, fit = NA, chain_id, ...)

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.
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.
seed	Random seed.
stan.model	Logical, if stan model should be returned.
fit	Fitted stan object.
chain_id	Chain id.
cl	Cluster object or integer, for parallel computations.
...	Further arguments.

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, [e-mail]

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/dcextras documentation built on May 14, 2019, 7:43 p.m.