abc: The ABC algorithm

Description

The approximate Bayesian computation (ABC) algorithm for estimating the parameters of a partially-observed Markov process.

Usage

## S3 method for class 'pomp':
abc(object, Nabc = 1, start, pars,
    rw.sd, probes, scale, epsilon,
    verbose = getOption("verbose"), ...)
## S3 method for class 'probed.pomp':
abc(object, probes,
    verbose = getOption("verbose"), ...)
## S3 method for class 'abc':
abc(object, Nabc, start, pars,
    rw.sd, probes, scale, epsilon,
    verbose = getOption("verbose"), ...)
## S3 method for class 'abc':
continue(object, Nabc = 1, \dots)

Arguments

object

An object of class pomp.

Nabc

The number of ABC iterations to perform.

start

named numeric vector; the starting guess of the parameters.

pars

optional character vector naming the ordinary parameters to be estimated. Every parameter named in pars must have a positive random-walk standard deviation specified in rw.sd. Leaving pars unspecified is equi

rw.sd

numeric vector with names; used to parameterize a Gaussian random walk MCMC proposal. The random walk is only applied to parameters named in pars. The algorithm requires that the random walk be nontrivial, so each element in rw.

probes

scale

epsilon

verbose

logical; if TRUE, print progress reports.

...

Additional arguments. These are currently ignored.

Value

An object of class abc. This class inherits from class probed.pomp and contains the following additional slots: [object Object],[object Object],[object Object]

Re-running ABC Iterations

To re-run a sequence of ABC iterations, one can use the abc method on a abc object. By default, the same parameters used for the original ABC run are re-used (except for tol, max.fail, and verbose, the defaults of which are shown above). If one does specify additional arguments, these will override the defaults.

Continuing ABC Iterations

One can continue a series of ABC iterations from where one left off using the continue method. A call to abc to perform Nabc=m iterations followed by a call to continue to perform Nabc=n iterations will produce precisely the same effect as a single call to abc to perform Nabc=m+n iterations. By default, all the algorithmic parameters are the same as used in the original call to abc. Additional arguments will override the defaults.

Details

TO APPEAR.

References

T. Toni and M. P. H. Stumpf, Simulation-based model selection for dynamical systems in systems and population biology, Bioinformatics 26:104--110, 2010.

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. H. Stumpf, Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems Journal of the Royal Society, Interface 6:187--202, 2009.