| update.mcmc.list | R Documentation |
Automatic updating of an MCMC object until a desired statistic value reached.
updated.model(object, ...)
## S3 method for class 'mcmc.list'
update(object, fun,
times = 1, n.update = 0, n.iter, thin, ...)
object |
A fitted MCMC object ('mcmc.list' class for example), with
|
fun |
A function that evaluates convergence of the MCMC chains,
must return logical result. See Examples.
The iterative updating quits when return value is |
times |
Number of times the updating should be repeated.
If |
n.update |
Number of updating iterations. The default 0 indicates,
that only |
n.iter |
Number of iterations for sampling and evaluating |
thin |
Thinning value. If missing, value is taken from |
... |
Other arguments passed to |
updated.model can be used to retrieve the updated model
from an MCMC object fitted via the function jags.fit
and dc.fit (with flavour = "jags").
The update method is a wrapper for this purpose,
specifically designed
for the case when MCMC convergence is problematic. A function
is evaluated on the updated model in each iteration of
the updating process,
and an MCMC object is returned when iteration ends,
or when the evaluated
function returns TRUE value.
n.update and n.iter can be vectors, if lengths are
shorter then times, values are recycled.
Data cloning information is preserved.
updated.model returns the state of the JAGS model after updating
and sampling. This can be further updated by the function
update.jags
and sampled by coda.samples if
convergence diagnostics were not satisfactory.
update returns an MCMC object with
"updated.model" attribute.
Peter Solymos, solymos@ualberta.ca
jags.fit, coda.samples,
update.jags
## 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, 1.0E-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 <- 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")
## fit the model with JAGS
regmod <- jags.fit(jdata, jpara, jfun, n.chains = 3)
## get the updated model
upmod <- updated.model(regmod)
upmod
## automatic updating
## using R_hat < 1.1 as criteria
critfun <- function(x)
all(gelman.diag(x)$psrf[,1] < 1.1)
mod <- update(regmod, critfun, 5)
## update just once
mod2 <- update(regmod)
summary(mod)
## End(Not run)