dclone (version 1.2-0)

write.jags.model: Write and remove model file

Description

Writes or removes a BUGS model file to or from the hard drive.

Usage

write.jags.model(model, filename = "model.bug", dir = getwd())
clean.jags.model(filename = "model.bug", dir = getwd())
custommodel(model, exclude = NULL)

Arguments

model

JAGS model to write onto the hard drive (see Example). For write.jags.model, it can be name of a file or a function, or it can be an 'custommodel' object returned by custommodel. custommodel can take its mod

filename

Character, the name of the file to write/remove.

dir

Optional argument for directory where to write or look for the file to remove.

exclude

Numeric, lines of the model to exclude (see Details).

Value

• write.jags.model invisibly returns the name of the file that was written eventually (possibly including random string). The function tries to avoid overwriting existing files. clean.jags.model invisibly returns the result of file.remove (logical). Original working directory is then restored. custommodel returns an object of class 'custommodel', which is a character vector.

encoding

UTF-8

Details

write.jags.model is built upon the function write.model of the R2WinBUGS package. clean.jags.model is built upon the function file.remove, and intended to be used internally to clean the JAGS model file up after estimating sessions, ideally via the on.exit function. The function custommodel can be used to exclude some lines of the model. This is handy when there are variations of the same model. write.jags.model accepts results returned by custommodel.

See Also

write.model, file.remove

Examples

## simple regression example from the JAGS manual
jfun <- function() {
    for (i in 1:N) {
        Y[i] ~ dnorm(mu[i], tau)
        mu[i] <- alpha + beta * (x[i] - x.bar)
    }
    x.bar <- mean(x)
    alpha ~ dnorm(0.0, 1.0E-4)
    beta ~ dnorm(0.0, 1.0E-4)
    sigma <- 1.0/sqrt(tau)
    tau ~ dgamma(1.0E-3, 1.0E-3)
}
## data generation
set.seed(1234)
N <- 100
alpha <- 1
beta <- -1
sigma <- 0.5
x <- runif(N)
linpred <- model.matrix(~x) %*% c(alpha, beta)
Y <- rnorm(N, mean = linpred, sd = sigma)
## list of data for the model
jdata <- list(N = N, Y = Y, x = x)
## what to monitor
jpara <- c("alpha", "beta", "sigma")
## write model onto hard drive
jmodnam <- write.jags.model(jfun)
## fit the model
regmod <- jags.fit(jdata, jpara, jmodnam, n.chains = 3)
## cleanup
clean.jags.model(jmodnam)
## model summary
summary(regmod)
## let's customize this model
jfun <- function() {
    for (i in 1:n) {
        Y[i] ~ dpois(lambda[i])
        Y[i] <- alpha[i] + inprod(X[i,], beta[1,])
        log(lambda[i]) <- alpha[i] + inprod(X[i,], beta[1,])
        alpha[i] ~ dnorm(0, 1/sigma^2)
    }
    for (j in 1:np) {
        beta[1,j] ~ dnorm(0, 0.001)
    }
    sigma ~ dlnorm(0, 0.001)
}
custommodel(jfun)
## GLMM
custommodel(jfun, 4)
## LM
custommodel(jfun, c(3,5))