kalman: Ensemble Kalman filters

In pomp: Statistical Inference for Partially Observed Markov Processes

Ensemble Kalman filters

Description

The ensemble Kalman filter and ensemble adjustment Kalman filter.

Usage

## S4 method for signature 'data.frame'
enkf(
  data,
  Np,
  params,
  rinit,
  rprocess,
  emeasure,
  vmeasure,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'kalmand_pomp'
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'data.frame'
eakf(
  data,
  Np,
  params,
  rinit,
  rprocess,
  emeasure,
  vmeasure,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
eakf(data, Np, ..., verbose = getOption("verbose", FALSE))

Arguments

data	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation. Internally, data will be coerced to an array with storage-mode double.
Np	integer; the number of particles to use, i.e., the size of the ensemble.
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 rinit specification.
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 rprocess specification for the documentation on these plugins.
emeasure	the expectation of the measured variables, conditional on the latent state. This can be specified as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting emeasure=NULL removes the emeasure component. For more information, see emeasure specification.
vmeasure	the covariance of the measured variables, conditional on the latent state. This can be specified as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting vmeasure=NULL removes the vmeasure component. For more information, see vmeasure specification.
...	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 userdata for information on how to use this facility.
verbose	logical; if TRUE, diagnostic messages will be printed to the console.

Value

An object of class ‘kalmand_pomp’.

Note for Windows users

Some Windows users report problems when using C snippets in parallel computations. These appear to arise when the temporary files created during the C snippet compilation process are not handled properly by the operating system. To circumvent this problem, use the cdir and cfile options to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.

Author(s)

Aaron A. King

References

\Evensen

1994

\Anderson

2001

\Evensen

2009

See Also

kalmanFilter

More on sequential Monte Carlo methods: bsmc2(), cond_logLik(), eff_sample_size(), filter_mean(), filter_traj(), mif2(), pfilter(), pmcmc(), pred_mean(), pred_var(), saved_states(), wpfilter()

More on pomp elementary algorithms: elementary_algorithms, pfilter(), pomp-package, probe(), simulate(), spect(), trajectory(), wpfilter()

pomp documentation built on Aug. 8, 2023, 1:08 a.m.