The ensemble Kalman filter and ensemble adjustment Kalman filter.
# S4 method for data.frame
enkf(
data,
Np,
params,
rinit,
rprocess,
emeasure,
vmeasure,
...,
verbose = getOption("verbose", FALSE)
)# S4 method for pomp
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))
# S4 method for kalmand_pomp
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))
# S4 method for data.frame
eakf(
data,
Np,
params,
rinit,
rprocess,
emeasure,
vmeasure,
...,
verbose = getOption("verbose", FALSE)
)
# S4 method for pomp
eakf(data, Np, ..., verbose = getOption("verbose", FALSE))
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 internally coerced to an array with storage-mode double.
integer; the number of particles to use, i.e., the size of the ensemble.
optional; named numeric vector of parameters.
This will be coerced internally to storage mode double.
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.
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.
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.
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.
logical; if TRUE, diagnostic messages will be printed to the console.
An object of class ‘kalmand_pomp’.
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.
G. Evensen. 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, 1994.
J.L. Anderson. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review 129, 2884--2903, 2001.
G. Evensen. Data assimilation: the ensemble Kalman filter. Springer-Verlag, 2009.
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()