How to add vertical line to posterior density plots using plot.mcmc?
Pergunta
I often run JAGS models on simulated data with known parameters. I like the default plot method for mcmc
objects. However, I would like to add an abline(v=TRUE_VALUE)
for each parameter that is modelled. This would give me a quick check for whether the posterior is reasonable.
Of course I could do this manually, or presumably reinvent the wheel and write my own function. But I was wondering if there is an elegant way that builds on the existing plot
method.
Here's a worked example:
require(rjags)
require(coda)
# simulatee data
set.seed(4444)
N <- 100
Mu <- 100
Sigma <- 15
y <- rnorm(n=N, mean=Mu, sd=Sigma)
jagsdata <- list(y=y)
jags.script <- "
model {
for (i in 1:length(y)) {
y[i] ~ dnorm(mu, tau)
}
mu ~ dnorm(0, 0.001)
sigma ~ dunif(0, 1000)
tau <- 1/sigma^2
}"
mod1 <- jags.model(textConnection(jags.script), data=jagsdata, n.chains=4,
n.adapt=1000)
update(mod1, 200) # burn in
mod1.samples <- coda.samples(model=mod1,
variable.names=c('mu', 'sigma'),
n.iter=1000)
plot(mod1.samples)
I just want to run something like abline(v=100)
for mu and abline(v=15)
for sigma. Of course in many other examples, I would have 5, 10, 20 or more parameters of interest. Thus, I'm interested in being able to supply a vector of true values for named parameters.
I've had a look at getAnywhere(plot.mcmc)
. Would modifying that be a good way to go?
Solução
Okay. So I modified plot.mcmc
to look like this:
my.plot.mcmc <- function (x, trace = TRUE, density = TRUE, smooth = FALSE, bwf,
auto.layout = TRUE, ask = FALSE, parameters, ...)
{
oldpar <- NULL
on.exit(par(oldpar))
if (auto.layout) {
mfrow <- coda:::set.mfrow(Nchains = nchain(x), Nparms = nvar(x),
nplots = trace + density)
oldpar <- par(mfrow = mfrow)
}
for (i in 1:nvar(x)) {
y <- mcmc(as.matrix(x)[, i, drop = FALSE], start(x),
end(x), thin(x))
if (trace)
traceplot(y, smooth = smooth, ...)
if (density) {
if (missing(bwf)) {
densplot(y, ...); abline(v=parameters[i])
} else densplot(y, bwf = bwf, ...)
}
if (i == 1)
oldpar <- c(oldpar, par(ask = ask))
}
}
Then running the command
my.plot.mcmc(mod1.samples, parameters=c(Mu, Sigma))
produces this
Note that parameters
must be a vector of values in the same sort order as JAGS sorts variables, which seems to be alphabetically and then numerically for vectors.
Lessons learnt
- Simply writing a new
plot.mcmc
didn't work by default presumably because of namespaces. So I just created a new function - I had to change
set.mfrow
tocoda:::set.mfrow
presumably also because of namespaces. - I changed ask to
ask=FALSE
, because RStudio permits browsing through figures.
I'd be happy to hear any suggestions about better ways of overriding or adapting existing S3 methods.