nimbleNoBounds (version 1.0.2)

dLogLnorm: Log transformed log-normal distribution.

Description

dLogLnorm and rLogLnorm provide a log-transformed log-normal distribution.

Usage

dLogLnorm(x, meanlog = 0, sdlog = 1, log = 0)
rLogLnorm(n = 1, meanlog = 0, sdlog = 1)

Value

The density or log density of x, such that x = log(y) and y ~ dlnorm(meanlog,sdlog).

Arguments

x

A continuous random variable on the real line. Let y=exp(x). Then y ~ dlnorm(meanlog, sdlog).

meanlog

mean of the distribution on the log scale with default values of ‘0’.

sdlog

standard deviation of the distribution on the log scale with default values of ‘1’.

log

Logical flag to toggle returning the log density.

n

Number of random variables. Currently limited to 1, as is common in nimble. See ?replicate for an alternative.

Author

David R.J. Pleydell

Examples

## Create a log-normal random variable, and transform it to the log scale
n       = 100000
meanlog = -3
sdlog   = 0.1
y       = rlnorm(n=n, meanlog=meanlog, sdlog=sdlog)
x       = log(y)

## Plot histograms of the two random variables
oldpar <- par()
par(mfrow=n2mfrow(2))
## Plot 1
hist(x, n=100, freq=FALSE)
curve(dLogLnorm(x, meanlog=meanlog, sdlog=sdlog), -4, -2, n=1001, col="red", add=TRUE, lwd=3)
## Plot 2: back-transformed
xNew = replicate(n=n, rLogLnorm(n=1, meanlog=meanlog, sdlog=sdlog))
yNew   = exp(xNew)
hist(yNew, n=100, freq=FALSE, xlab="exp(x)")
curve(dlnorm(x, meanlog=meanlog, sdlog=sdlog), 0, 0.1, n=1001, col="red", lwd=3, add=TRUE)
par(oldpar)

## Create a NIMBLE model that uses this transformed distribution
code = nimbleCode({
  log(y) ~ dLogLnorm(meanlog=meanlog, sdlog=sdlog)
})

# \donttest{
## Build & compile the model
const = list (meanlog=meanlog, sdlog=sdlog)
modelR = nimbleModel(code=code, const=const)
simulate(modelR)
modelC = compileNimble(modelR)

## Configure, build and compile an MCMC
conf  = configureMCMC(modelC)
mcmc  = buildMCMC(conf=conf)
cMcmc = compileNimble(mcmc)

## Run the MCMC
x = as.vector(runMCMC(mcmc=cMcmc, niter=50000))
y = exp(x)

## Plot MCMC output
oldpar <- par()
par(mfrow=n2mfrow(3))
## Plot 1: MCMC trajectory
plot(x, typ="l")
## Plot 2: taget density on unbounded sampling scale
hist(x, n=100, freq=FALSE)
curve(dLogLnorm(x, meanlog=meanlog, sdlog=sdlog), -4, -2, n=1001, col="red", lwd=3, add=TRUE)
## Plot 3: taget density on bounded scale
hist(y, n=100, freq=FALSE)
curve(dlnorm(x, meanlog=meanlog, sdlog=sdlog), 0, 0.1, n=1001, col="red", lwd=3, add=TRUE)
par(oldpar)
# }