abc: Approximate Bayesian computation
In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description Usage Arguments Running ABC Re-running ABC iterations Continuing ABC iterations Methods Author(s) References See Also

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

## S4 method for signature 'data.frame'
abc(data, Nabc = 1, proposal, scale, epsilon,
  probes, params, rinit, rprocess, rmeasure, dprior, ...,
  verbose = getOption("verbose", FALSE))

## S4 method for signature 'pomp'
abc(data, Nabc = 1, proposal, scale, epsilon, probes,
  ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'probed_pomp'
abc(data, probes, ...,
  verbose = getOption("verbose", FALSE))

## S4 method for signature 'abcd_pomp'
abc(data, Nabc, proposal, scale, epsilon, probes,
  ..., verbose = getOption("verbose", FALSE))

`data`	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation.
`Nabc`	the number of ABC iterations to perform.
`proposal`	optional function that draws from the proposal distribution. Currently, the proposal distribution must be symmetric for proper inference: it is the user's responsibility to ensure that it is. Several functions that construct appropriate proposal function are provided: see MCMC proposals for more information.
`scale`	named numeric vector of scales.
`epsilon`	ABC tolerance.
`probes`	a single probe or a list of one or more probes. A probe is simply a scalar- or vector-valued function of one argument that can be applied to the data array of a ‘pomp’. A vector-valued probe must always return a vector of the same size. A number of useful probes are provided with the package: see basic probes.
`params`	optional; named numeric vector of parameters. This will be coerced internally to storage mode `double`.
`rinit`	simulator of the initial-state distribution. This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `rinit=NULL` sets the initial-state simulator to its default. For more information, see here.
`rprocess`	simulator of the latent state process, specified using one of the rprocess plugins. Setting `rprocess=NULL` removes the latent-state simulator. For more information, see the documentation on these plugins.
`rmeasure`	simulator of the measurement model, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `rmeasure=NULL` removes the measurement model simulator. For more information, see here.
`dprior`	optional; prior distribution density evaluator, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. For more information, see here. Setting `dprior=NULL` resets the prior distribution to its default, which is a flat improper prior.
`...`	additional arguments supply new or modify existing model characteristics or components. See `pomp` for a full list of recognized arguments. When named arguments not recognized by `pomp` are provided, these are made available to all basic components via the so-called userdata facility. This allows the user to pass information to the basic components outside of the usual routes of covariates (`covar`) and model parameters (`params`). See the userdata documentation here for information on how to use this facility.
`verbose`	logical; if `TRUE`, diagnostic messages will be printed to the console.

abc returns an object of class ‘abcd_pomp’. One or more ‘abcd_pomp’ objects can be joined to form an ‘abcList’ object.

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

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.

The following can be applied to the output of an abc operation:

plot: produces a series of diagnostic plots
traces: produces a mcmc object, to which the various coda convergence diagnostics can be applied

Edward L. Ionides, Aaron A. King

J.-M. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder, Approximate Bayesian computational methods. Statistics and Compuing 22:1167–1180, 2012.

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.

MCMC proposals

Other summary statistics methods: basic_probes, probe.match, probe, spect

Other pomp parameter estimation methods: bsmc2, kalman, mif2, nlf, pmcmc, pomp2-package, probe.match, spect.match