rcppbugs (version 0.1.4.2)

run.model: Create and run rcppbugs models.

Description

The create.model function creates a model object. The run.model function simulates an mcmc chain of the nodes in the model. After the completion of run.model, the history of the run is returned as a named list.

Usage

create.model(...)
run.model(m, iterations, burn, adapt, thin)
get.ar(x)

Arguments

m

the rcppbugs model object.

iterations

how many iterations to sample.

burn

how many iterations to use for burnin.

adapt

how many iterations to use for the adaptive period.

thin

how frequently to record traces of the model nodes.

...

rcppbugs objects to use as the nodes of the model.

x

the result of an rcppbugs run.

Value

create.model returns a mcmc.model model object. run.model returns a named list containing the historical traces of the model run. get.ar returns the acceptance ratio of an MCMC run

References

https://github.com/armstrtw/CppBugs

See Also

logp

Examples

library(rcppbugs)


NR <- 1e2L
NC <- 2L
y <- matrix(rnorm(NR,1) + 10,nr=NR,nc=1L)
X <- matrix(nr=NR,nc=NC)
X[,1] <- 1
X[,2] <- y + rnorm(NR)/2 - 10

## RCppBugs Model
b <- mcmc.normal(rnorm(NC),mu=0,tau=0.0001)
tau.y <- mcmc.gamma(sd(as.vector(y)),alpha=0.1,beta=0.1)
y.hat <- linear(X,b)
y.lik <- mcmc.normal(y,mu=y.hat,tau=tau.y,observed=TRUE)
m <- create.model(b, tau.y, y.hat, y.lik)


runtime <- system.time(ans <- run.model(m, iterations=1e2L, burn=1e2L, adapt=1e3L, thin=10L))
print(get.ar(ans))

print(apply(ans[["b"]],2,mean))