kalman {pomp} | R Documentation |
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, |
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 |
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 |
rprocess |
simulator of the latent state process, specified using one of the rprocess plugins.
Setting |
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 |
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 |
... |
additional arguments are passed to |
verbose |
logical; if |
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
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. doi:10.1029/94JC00572.
J.L. Anderson. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review 129, 2884–2903, 2001. doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.
G. Evensen. Data assimilation: the ensemble Kalman filter. Springer-Verlag, 2009. doi:10.1007/978-3-642-03711-5.
See Also
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()