wpfilter {pomp}R Documentation

Weighted particle filter


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


## S4 method for signature 'data.frame'
  trigger = 1,
  target = 0.5,
  verbose = getOption("verbose", FALSE)

## S4 method for signature 'pomp'
  trigger = 1,
  target = 0.5,
  verbose = getOption("verbose", FALSE)

## S4 method for signature 'wpfilterd_pomp'
wpfilter(data, Np, trigger, target, ..., 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.


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


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.


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_spec.


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_spec for the documentation on these plugins.


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_spec.


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


numeric; target power.


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.


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.


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



the estimated log likelihood


the estimated conditional log likelihood


the (time-dependent) estimated effective sample size


coerce to a data frame


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 (described here) to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.


Aaron A. King


M.S. Arulampalam, S. Maskell, N. Gordon, & T. Clapp. A Tutorial on Particle Filters for Online Nonlinear, Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50, 174–188, 2002.

See Also

More on pomp elementary algorithms: elementary_algorithms, pfilter(), pomp-package, probe(), simulate(), spect(), trajectory()

More on particle-filter based methods in pomp: bsmc2(), cond.logLik(), eff.sample.size(), filter.mean(), filter.traj(), kalman, mif2(), pfilter(), pmcmc(), pred.mean(), pred.var(), saved.states()

[Package pomp version Index]