adaptMetropGibbs: Adaptive Metropolis within Gibbs algorithm

In spBayes: Univariate and Multivariate Spatial-Temporal Modeling

Adaptive Metropolis within Gibbs algorithm

Description

Markov chain Monte Carlo for continuous random vector using an adaptive Metropolis within Gibbs algorithm.

Usage

adaptMetropGibbs(ltd, starting, tuning=1, accept.rate=0.44,
                 batch = 1, batch.length=25, report=100,
                 verbose=TRUE, ...)

Arguments

ltd	an R function that evaluates the log target density of the desired equilibrium distribution of the Markov chain. First argument is the starting value vector of the Markov chain. Pass variables used in the ltd via the ... argument of aMetropGibbs.
starting	a real vector of parameter starting values.
tuning	a scalar or vector of initial Metropolis tuning values. The vector must be of length(starting). If a scalar is passed then it is expanded to length(starting).
accept.rate	a scalar or vector of target Metropolis acceptance rates. The vector must be of length(starting). If a scalar is passed then it is expanded to length(starting).
batch	the number of batches.
batch.length	the number of sampler iterations in each batch.
report	the number of batches between acceptance rate reports.
verbose	if TRUE, progress of the sampler is printed to the screen. Otherwise, nothing is printed to the screen.
...	currently no additional arguments.

Value

A list with the following tags:

p.theta.samples	a coda object of posterior samples for the parameters.
acceptance	the Metropolis acceptance rate at the end of each batch.
ltd	ltd
accept.rate	accept.rate
batch	batch
batch.length	batch.length
proc.time	the elapsed CPU and wall time (in seconds).

Note

This function is a rework of Rosenthal (2007) with some added niceties.

Author(s)

Andrew O. Finley [e-mail],
Sudipto Banerjee [e-mail]

References

Roberts G.O. and Rosenthal J.S. (2006). Examples of Adaptive MCMC. http://probability.ca/jeff/ftpdir/adaptex.pdf Preprint.

Rosenthal J.S. (2007). AMCMC: An R interface for adaptive MCMC. Computational Statistics and Data Analysis. 51:5467-5470.

Examples

## Not run: 
rmvn <- function(n, mu=0, V = matrix(1)){
  p <- length(mu)
  if(any(is.na(match(dim(V),p))))
    stop("Dimension problem!")
  D <- chol(V)
  t(matrix(rnorm(n*p), ncol=p)%*%D + rep(mu,rep(n,p)))
}

###########################
##Fit a spatial regression
###########################
set.seed(1)
n <- 50
x <- runif(n, 0, 100)
y <- runif(n, 0, 100)

D <- as.matrix(dist(cbind(x, y)))

phi <- 3/50
sigmasq <- 50
tausq <- 20
mu <- 150

s <- (sigmasq*exp(-phi*D))
w <-  rmvn(1, rep(0, n), s)
Y <- rmvn(1, rep(mu, n) + w, tausq*diag(n))
X <- as.matrix(rep(1, length(Y)))

##Priors
##IG sigma^2 and tau^2
a.sig <- 2 
b.sig <- 100
a.tau <- 2
b.tau <- 100

##Unif phi
a.phi <- 3/100
b.phi <- 3/1

##Functions used to transform phi to continuous support.
logit <- function(theta, a, b){log((theta-a)/(b-theta))}
logit.inv <- function(z, a, b){b-(b-a)/(1+exp(z))}

##Metrop. target
target <- function(theta){
  
  mu.cand <- theta[1]
  sigmasq.cand <- exp(theta[2])
  tausq.cand <- exp(theta[3])
  phi.cand <- logit.inv(theta[4], a.phi, b.phi)

  Sigma <- sigmasq.cand*exp(-phi.cand*D)+tausq.cand*diag(n)
  SigmaInv <- chol2inv(chol(Sigma))
  logDetSigma <- determinant(Sigma, log=TRUE)$modulus[1]
  
  out <- (
          ##Priors
          -(a.sig+1)*log(sigmasq.cand) - b.sig/sigmasq.cand
          -(a.tau+1)*log(tausq.cand) - b.tau/tausq.cand
          ##Jacobians
          +log(sigmasq.cand) + log(tausq.cand) 
          +log(phi.cand - a.phi) + log(b.phi -phi.cand) 
          ##Likelihood
          -0.5*logDetSigma-0.5*(t(Y-X%*%mu.cand)%*%SigmaInv%*%(Y-X%*%mu.cand))
          )
  
  return(out)
}


##Run a couple chains
n.batch <- 500
batch.length <- 25

inits <- c(0, log(1), log(1), logit(3/10, a.phi, b.phi))
chain.1 <- adaptMetropGibbs(ltd=target, starting=inits,
                            batch=n.batch, batch.length=batch.length, report=100)

inits <- c(500, log(100), log(100), logit(3/90, a.phi, b.phi))
chain.2 <- adaptMetropGibbs(ltd=target, starting=inits,
                            batch=n.batch, batch.length=batch.length, report=100)

##Check out acceptance rate just for fun
plot(mcmc.list(mcmc(chain.1$acceptance), mcmc(chain.2$acceptance)))

##Back transform
chain.1$p.theta.samples[,2] <- exp(chain.1$p.theta.samples[,2])
chain.1$p.theta.samples[,3] <- exp(chain.1$p.theta.samples[,3])
chain.1$p.theta.samples[,4] <- 3/logit.inv(chain.1$p.theta.samples[,4], a.phi, b.phi)

chain.2$p.theta.samples[,2] <- exp(chain.2$p.theta.samples[,2])
chain.2$p.theta.samples[,3] <- exp(chain.2$p.theta.samples[,3])
chain.2$p.theta.samples[,4] <- 3/logit.inv(chain.2$p.theta.samples[,4], a.phi, b.phi)

par.names <- c("mu", "sigma.sq", "tau.sq", "effective range (-log(0.05)/phi)")
colnames(chain.1$p.theta.samples) <- par.names
colnames(chain.2$p.theta.samples) <- par.names

##Discard burn.in and plot and do some convergence diagnostics
chains <- mcmc.list(mcmc(chain.1$p.theta.samples), mcmc(chain.2$p.theta.samples))
plot(window(chains, start=as.integer(0.5*n.batch*batch.length)))

gelman.diag(chains)

##########################
##Example of fitting a
##a non-linear model
##########################
##Example of fitting a non-linear model
set.seed(1)

########################################################
##Simulate some data.
########################################################
a <- 0.1 #-Inf < a < Inf
b <- 0.1 #b > 0
c <- 0.2 #c > 0
tau.sq <- 0.1 #tau.sq > 0

fn <- function(a,b,c,x){
  a+b*exp(x/c)
}

n <- 200
x <- seq(0,1,0.01)
y <- rnorm(length(x), fn(a,b,c,x), sqrt(tau.sq))

##check out your data
plot(x, y)

########################################################
##The log target density
########################################################
##Define the log target density used in the Metrop.
ltd <- function(theta){

  ##extract and transform as needed
  a <- theta[1]
  b <- exp(theta[2])
  c <- exp(theta[3])
  tau.sq <- exp(theta[4])

  y.hat <- fn(a, b, c, x)

  ##likelihood
  logl <- sum(dnorm(y, y.hat, sqrt(tau.sq), log=TRUE))

  ##priors IG on tau.sq and normal on a and transformed b, c, d
  logl <- (logl
           -(IG.a+1)*log(tau.sq)-IG.b/tau.sq
           +sum(dnorm(theta[1:3], N.mu, N.v, log=TRUE))
           )
  
  ##Jacobian adjustment for tau.sq
  logl <- logl+log(tau.sq)
  
  return(logl)  
}

########################################################
##The rest
########################################################

##Priors
IG.a <- 2
IG.b <- 0.01

N.mu <- 0
N.v <- 10

theta.tuning <- c(0.01, 0.01, 0.005, 0.01)

##Run three chains with different starting values
n.batch <- 1000
batch.length <- 25

theta.starting <- c(0, log(0.01), log(0.6), log(0.01))
run.1 <- adaptMetropGibbs(ltd=ltd, starting=theta.starting, tuning=theta.tuning,
                          batch=n.batch, batch.length=batch.length, report=100)

theta.starting <- c(1.5, log(0.05), log(0.5), log(0.05))
run.2 <- adaptMetropGibbs(ltd=ltd, starting=theta.starting, tuning=theta.tuning,
                          batch=n.batch, batch.length=batch.length, report=100)

theta.starting <- c(-1.5, log(0.1), log(0.4), log(0.1))
run.3 <- adaptMetropGibbs(ltd=ltd, starting=theta.starting, tuning=theta.tuning,
                          batch=n.batch, batch.length=batch.length, report=100)

##Back transform
samples.1 <- cbind(run.1$p.theta.samples[,1], exp(run.1$p.theta.samples[,2:4]))
samples.2 <- cbind(run.2$p.theta.samples[,1], exp(run.2$p.theta.samples[,2:4]))
samples.3 <- cbind(run.3$p.theta.samples[,1], exp(run.3$p.theta.samples[,2:4]))

samples <- mcmc.list(mcmc(samples.1), mcmc(samples.2), mcmc(samples.3))

##Summary 
plot(samples, density=FALSE)
gelman.plot(samples)

burn.in <- 5000

fn.pred <- function(theta,x){
  a <- theta[1]
  b <- theta[2]
  c <- theta[3]
  tau.sq <- theta[4]
  
  rnorm(length(x), fn(a,b,c,x), sqrt(tau.sq))
}

post.curves <- t(apply(samples.1[burn.in:nrow(samples.1),], 1, fn.pred, x))

post.curves.quants <- summary(mcmc(post.curves))$quantiles

plot(x, y, pch=19, xlab="x", ylab="f(x)")
lines(x, post.curves.quants[,1], lty="dashed", col="blue")
lines(x, post.curves.quants[,3])
lines(x, post.curves.quants[,5], lty="dashed", col="blue")



## End(Not run)

spBayes documentation built on May 17, 2022, 5:07 p.m.