Bhat (version 0.9-12)

global: Random search for a global function minimum

Description

This function generates MCMC samples from a (posterior) density function f (not necessarily normalized) in search of a global minimum of f. It uses a simple Metropolis algorithm to generate the samples. Global monitors the mcmc samples and returns the minimum value of f, as well as a sample covariance (covm) that can be used as input for the Bhat function mymcmc.

Usage

global(
  x,
  nlogf,
  beta = 1,
  mc = 1000,
  scl = 2,
  skip = 1,
  nfcn = 0,
  plot = FALSE
)

Arguments

x

a list with components 'label' (of mode character), 'est' (the parameter vector with the initial guess), 'low' (vector with lower bounds), and 'upp' (vector with upper bounds)

nlogf

negative log of the density function (not necessarily normalized)

beta

'inverse temperature' parameter

mc

length of MCMC search run

scl

not used

skip

number of cycles skipped for graphical output

nfcn

number of function calls

plot

logical variable. If TRUE the chain and the negative log density (nlogf) is plotted

Value

list with the following components:

fmin

minimum value of nlogf for the samples obtained

xmin

parameter values at fmin

covm

covariance matrix of differences between consecutive samples in chain

Details

standard output reports a summary of the acceptance fraction, the current values of nlogf and the parameters for every (100*skip) th cycle. Plotted chains show values only for every (skip) th cycle.

References

too numerous to be listed here

See Also

dfp, newton, logit.hessian mymcmc

Examples

# NOT RUN {
# generate some Poisson counts on the fly
  dose <- c(rep(0,50),rep(1,50),rep(5,50),rep(10,50))
  data <- cbind(dose,rpois(200,20*(1+dose*.5*(1-dose*0.05))))

# neg. log-likelihood of Poisson model with 'linear-quadratic' mean: 
  nlogf <- function (x) { 
  ds <- data[, 1]
  y  <- data[, 2]
  g <- x[1] * (1 + ds * x[2] * (1 - x[3] * ds)) 
  return(sum(g - y * log(g)))
  }

# initialize global search
  x <- list(label=c("a","b","c"), est=c(10, 0.25, 0.05), low=c(0,0,0), upp=c(100,10,.1))
# samples from posterior density (~exp(-nlogf))) with non-informative
# (random uniform) priors for "a", "b" and "c".
out <- global(x, nlogf, beta = 1., mc=1000, scl=2, skip=1, nfcn = 0, plot=TRUE)
# start MCMC from some other point: e.g. try x$est <- c(16,.2,.02)

# }