kalman: Ensemble Kalman filters

In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description

The ensemble Kalman filter and ensemble adjustment Kalman filter.

Usage

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

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

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

## S4 method for signature 'pomp'
eakf(data, Np, C, R, ..., 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.
Np	the number of particles to use.
h	function returning the expected value of the observation given the state.
R	matrix; variance of the measurement noise.
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.
...	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.
C	matrix converting state vector into expected value of the observation.

Value

An object of class ‘kalmand_pomp’.

Author(s)

Aaron A. King

References

Evensen, G. (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics Journal of Geophysical Research: Oceans 99:10143–10162

Evensen, G. (2009) Data assimilation: the ensemble Kalman filter Springer-Verlag.

Anderson, J. L. (2001) An Ensemble Adjustment Kalman Filter for Data Assimilation Monthly Weather Review 129:2884–2903

See Also

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

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.