Description Usage Arguments Value Methods References See Also
An implementation of a parameter estimation algorithm combining the intermediate resampling scheme of the guided intermediate resampling filter of Park and Ionides (2020) and the parameter perturbation scheme of Ionides et al. (2015) following the pseudocode in Asfaw, et al. (2020).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39  ## S4 method for signature 'missing'
igirf(data, ...)
## S4 method for signature 'ANY'
igirf(data, ...)
## S4 method for signature 'spatPomp'
igirf(
data,
Ngirf,
Np,
rw.sd,
cooling.type,
cooling.fraction.50,
Ninter,
lookahead = 1,
Nguide,
kind = c("bootstrap", "moment"),
tol = 1e300,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'igirfd_spatPomp'
igirf(
data,
Ngirf,
Np,
rw.sd,
cooling.type,
cooling.fraction.50,
Ninter,
lookahead,
Nguide,
kind = c("bootstrap", "moment"),
tol,
...,
verbose = getOption("verbose", FALSE)
)

data 
an object of class 
... 
If a 
Ngirf 
the number of iterations of parameterperturbed GIRF. 
Np 
The number of particles used within each replicate for the adapted simulations. 
rw.sd 
specification of the magnitude of the randomwalk perturbations that will be applied to some or all model parameters.
Parameters that are to be estimated should have positive perturbations specified here.
The specification is given using the ifelse(time==time[1],s,0). Likewise, ifelse(time==time[lag],s,0). See below for some examples. The perturbations that are applied are normally distributed with the specified s.d. If parameter transformations have been supplied, then the perturbations are applied on the transformed (estimation) scale. 
cooling.type 
specifications for the cooling schedule,
i.e., the manner and rate with which the intensity of the parameter perturbations is reduced with successive filtering iterations.

cooling.fraction.50 
specifications for the cooling schedule,
i.e., the manner and rate with which the intensity of the parameter perturbations is reduced with successive filtering iterations.

Ninter 
the number of intermediate resampling time points. 
lookahead 
The number of future observations included in the guide function. 
Nguide 
The number of simulations used to estimate state process uncertainty for each particle. 
kind 
One of two types of guide function construction. Defaults to 
tol 
If all of the guide function evaluations become too small (beyond floatingpoint precision limits), we set them to this value. 
verbose 
logical; if 
Upon successful completion, igirf()
returns an object of class
‘igirfd_spatPomp’. This object contains the convergence record of the iterative algorithm with
respect to the likelihood and the parameters of the model (which can be accessed using the traces
attribute) as well as a final parameter estimate, which can be accessed using the coef()
. The
algorithmic parameters used to run igirf()
are also included.
The following methods are available for such an object:
coef
gives the Monte Carlo maximum likelihood parameter estimate.
2020
\asfaw2020
Other particle filter methods:
abfir()
,
abf()
,
bpfilter()
,
enkf()
,
girf()
,
ienkf()
,
iubf()
Other spatPomp parameter estimation methods:
ienkf()
,
iubf()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.