pfilter {pomp}R Documentation

Particle filter

Description

A plain vanilla sequential Monte Carlo (particle filter) algorithm. Resampling is performed at each observation.

Usage

## S4 method for signature 'data.frame'
pfilter(
  data,
  Np,
  params,
  rinit,
  rprocess,
  dmeasure,
  pred.mean = FALSE,
  pred.var = FALSE,
  filter.mean = FALSE,
  filter.traj = FALSE,
  save.states = c("no", "weighted", "unweighted", "FALSE", "TRUE"),
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
pfilter(
  data,
  Np,
  pred.mean = FALSE,
  pred.var = FALSE,
  filter.mean = FALSE,
  filter.traj = FALSE,
  save.states = c("no", "weighted", "unweighted", "FALSE", "TRUE"),
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pfilterd_pomp'
pfilter(data, Np, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'objfun'
pfilter(data, ...)

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.

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.

pred.mean

logical; if TRUE, the prediction means are calculated for the state variables and parameters.

pred.var

logical; if TRUE, the prediction variances are calculated for the state variables and parameters.

filter.mean

logical; if TRUE, the filtering means are calculated for the state variables and parameters.

filter.traj

logical; if TRUE, a filtered trajectory is returned for the state variables and parameters. See filter_traj for more information.

save.states

character; If save.states="unweighted", the state-vector for each unweighted particle at each time is saved. If save.states="weighted", the state-vector for each weighted particle at each time is saved, along with the corresponding weight. If save.states="no", information on the latent states is not saved. "FALSE" is a synonym for "no" and "TRUE" is a synonym for "unweighted". To retrieve the saved states, applying saved_states to the result of the pfilter computation.

...

additional arguments are passed to pomp.

verbose

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

Value

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

Methods

logLik

the estimated log likelihood

cond_logLik

the estimated conditional log likelihood

eff_sample_size

the (time-dependent) estimated effective sample size

pred_mean, pred_var

the mean and variance of the approximate prediction distribution

filter_mean

the mean of the filtering distribution

filter_traj

retrieve one particle trajectory. Useful for building up the smoothing distribution.

saved_states

retrieve saved states

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(s)

Aaron A. King

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. doi:10.1109/78.978374.

A. Bhadra and E.L. Ionides. Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models. Statistics and Computing 26, 393–407, 2016. doi:10.1007/s11222-014-9513-x.

See Also

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

More on sequential Monte Carlo methods: bsmc2(), cond_logLik(), eff_sample_size(), filter_mean(), filter_traj(), kalman, mif2(), pmcmc(), pred_mean(), pred_var(), saved_states(), wpfilter()

More on full-information (i.e., likelihood-based) methods: bsmc2(), mif2(), pmcmc(), wpfilter()

Examples

pf <- pfilter(gompertz(),Np=1000)	## use 1000 particles

plot(pf)
logLik(pf)
cond_logLik(pf)			## conditional log-likelihoods
eff_sample_size(pf)             ## effective sample size
logLik(pfilter(pf))      	## run it again with 1000 particles

## run it again with 2000 particles
pf <- pfilter(pf,Np=2000,filter.mean=TRUE,filter.traj=TRUE,save.states="weighted")
fm <- filter_mean(pf) ## extract the filtering means
ft <- filter_traj(pf) ## one draw from the smoothing distribution
ss <- saved_states(pf,format="d") ## the latent-state portion of each particle

as(pf,"data.frame") |> head()

[Package pomp version 5.11.0.0 Index]