View source: R/write.jags.model.R
write.jags.model  R Documentation 
Writes or removes a BUGS model file to or from the hard drive.
write.jags.model(model, filename = "model.txt", digits = 5,
dir = tempdir(), overwrite = getOption("dcoptions")$overwrite)
clean.jags.model(filename = "model.txt")
custommodel(model, exclude = NULL, digits = 5)
model 
JAGS model to write onto the hard drive (see Example).
For 
digits 
Number of significant digits used in the output. 
filename 
Character, the name of the file to write/remove.
It can be a 
dir 
Optional argument for directory where to write the file.
The default is to use a temporary directory and use

overwrite 
Logical, if 
exclude 
Numeric, lines of the model to exclude (see 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 up the JAGS
model file after estimating sessions,
ideally via the on.exit
function.
It requires the full path as returned by write.jags.model
.
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
.
This is also the preferred way of including BUGS models into
R packages, because the function form often includes
undefined functions.
Use the %_%
operator if the model is a function and the model
contains truncation (I()
in WinBUGS, T()
in JAGS).
See explanation on help page of write.model
.
write.jags.model
invisibly returns the name of the file
that was written eventually (possibly including random string).
The return value includes the full path.
clean.jags.model
invisibly returns the result of
file.remove
(logical).
custommodel
returns an object of class 'custommodel',
which is a character vector.
Peter Solymos
write.model
, file.remove
## Not run:
## 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, 1.0E4)
beta ~ dnorm(0.0, 1.0E4)
sigma < 1.0/sqrt(tau)
tau ~ dgamma(1.0E3, 1.0E3)
}
## data generation
set.seed(1234)
N < 100
alpha < 1
beta < 1
sigma < 0.5
x < runif(N)
linpred < crossprod(t(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)
## End(Not run)
## let's customize this model
jfun2 < structure(
c(" model { ",
" 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) ",
" } "),
class = "custommodel")
custommodel(jfun2)
## GLMM
custommodel(jfun2, 4)
## LM
custommodel(jfun2, c(3,5))
## deparse when print
print(custommodel(jfun2), deparse=TRUE)
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