wpfilter: Weighted particle filter

Description

A sequential importance sampling (particle filter) algorithm. Unlike in pfilter, resampling is performed only when triggered by deficiency in the effective sample size.

Usage

# S4 method for data.frame
wpfilter(
  data,
  ...,
  Np,
  params,
  rinit,
  rprocess,
  dmeasure,
  trigger = 1,
  target = 0.5,
  verbose = getOption("verbose", FALSE)
)
# S4 method for pomp
wpfilter(
  data,
  ...,
  Np,
  trigger = 1,
  target = 0.5,
  verbose = getOption("verbose", FALSE)
)
# S4 method for wpfilterd_pomp
wpfilter(data, ..., Np, trigger, target, verbose = getOption("verbose", FALSE))

Value

An object of class ‘wpfilterd_pomp’, which extends class ‘pomp’. Information can be extracted from this object using the methods documented below.

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.

...

additional arguments are passed to pomp. This allows one to set, unset, or modify basic model components within a call to this function.

Np

the number of particles to use. This may be specified as a single positive integer, in which case the same number of particles will be used at each timestep. Alternatively, if one wishes the number of particles to vary across timesteps, one may specify Np either as a vector of positive integers of length

length(time(object,t0=TRUE))

or as a function taking a positive integer argument. In the latter case, Np(k) must be a single positive integer, representing the number of particles to be used at the k-th timestep: Np(0) is the number of particles to use going from timezero(object) to time(object)[1], Np(1), from timezero(object) to time(object)[1], and so on, while when T=length(time(object)), Np(T) is the number of particles to sample at the end of the time-series.

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.

dmeasure

evaluator of the measurement model density, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting dmeasure=NULL removes the measurement density evaluator. For more information, see dmeasure specification.

trigger

numeric; if the effective sample size becomes smaller than trigger * Np, resampling is triggered.

target

numeric; target power.

verbose

logical; if TRUE, diagnostic messages will be printed to the console.

Methods

logLik: the estimated log likelihood

cond_logLik

the estimated conditional log likelihood

eff_sample_size

the (time-dependent) estimated effective sample size

as.data.frame

coerce to a data frame

plot

diagnostic plots

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

Aaron A. King

Details

This function is experimental and should be considered in alpha stage. Both interface and underlying algorithms may change without warning at any time. Please explore the function and give feedback via the pomp Issues page.

References

M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear, non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50, 174--188, 2002. tools:::Rd_expr_doi("10.1109/78.978374").