mcmcensemble (version 3.1.0)

MCMCEnsemble: MCMC ensemble sampler

Description

Ensemble Markov Chain Monte Carlo sampler with different strategies to generate proposals. Either the stretch move as proposed by Goodman and Weare (2010), or a differential evolution jump move similar to Braak and Vrugt (2008).

Usage

MCMCEnsemble(
  f,
  inits,
  max.iter,
  n.walkers = 10 * ncol(inits),
  method = c("stretch", "differential.evolution"),
  coda = FALSE,
  ...
)

Value

• if coda = FALSE a list with:

  • samples: A three dimensional array of samples with dimensions walker x generation x parameter

  • log.p: A matrix with the log density evaluate for each walker at each generation.

• if coda = TRUE a list with:

  • samples: A object of class coda::mcmc.list containing all samples.

  • log.p: A matrix with the log density evaluate for each walker at each generation.

In both cases, there is an additional attribute (accessible via attr(res, "ensemble.sampler")) recording which ensemble sampling algorithm was used.

Arguments

f

function that returns a single scalar value proportional to the log probability density to sample from.

inits

A matrix (or data.frame) containing the starting values for the walkers. Each column is a variable to estimate and each row is a walker

max.iter

maximum number of function evaluations

n.walkers

number of walkers (ensemble size). An integer greater or equal than 2.

method

method for proposal generation, either "stretch", or "differential.evolution". This argument will be saved as an attribute in the output (see examples).

coda

logical. Should the samples be returned as coda::mcmc.list object? (defaults to FALSE)

...

further arguments passed to f

References

• ter Braak, C. J. F. and Vrugt, J. A. (2008) Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435–446, tools:::Rd_expr_doi("10.1007/s11222-008-9104-9")

• Goodman, J. and Weare, J. (2010) Ensemble samplers with affine invariance. Communications in Applied Mathematics and Computational Science, 5(1), 65–80, tools:::Rd_expr_doi("10.2140/camcos.2010.5.65")

Examples

## a log-pdf to sample from
p.log <- function(x) {
    B <- 0.03                              # controls 'bananacity'
    -x[1]^2/200 - 1/2*(x[2]+B*x[1]^2-100*B)^2
}

## set options and starting point
n_walkers <- 10
unif_inits <- data.frame(
  "a" = runif(n_walkers, 0, 1),
  "b" = runif(n_walkers, 0, 1)
)


## use stretch move
res1 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 300, n.walkers = n_walkers,
                     method = "stretch")

attr(res1, "ensemble.sampler")

str(res1)


## use stretch move, return samples as 'coda' object
res2 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 300, n.walkers = n_walkers,
                     method = "stretch", coda = TRUE)

attr(res2, "ensemble.sampler")

summary(res2$samples)
plot(res2$samples)


## use different evolution move, return samples as 'coda' object
res3 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 300, n.walkers = n_walkers,
                     method = "differential.evolution", coda = TRUE)

attr(res3, "ensemble.sampler")

summary(res3$samples)
plot(res3$samples)