spect: Power spectrum

In pomp: Statistical Inference for Partially Observed Markov Processes

Power spectrum

Description

Power spectrum computation and spectrum-matching for partially-observed Markov processes.

Usage

## S4 method for signature 'data.frame'
spect(
  data,
  vars,
  kernel.width,
  nsim,
  seed = NULL,
  transform.data = identity,
  detrend = c("none", "mean", "linear", "quadratic"),
  params,
  rinit,
  rprocess,
  rmeasure,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
spect(
  data,
  vars,
  kernel.width,
  nsim,
  seed = NULL,
  transform.data = identity,
  detrend = c("none", "mean", "linear", "quadratic"),
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'spectd_pomp'
spect(
  data,
  vars,
  kernel.width,
  nsim,
  seed = NULL,
  transform.data,
  detrend,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'spect_match_objfun'
spect(data, seed, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'objfun'
spect(data, seed = NULL, ...)

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.
vars	optional; names of observed variables for which the power spectrum will be computed. By default, the spectrum will be computed for all observables.
kernel.width	width parameter for the smoothing kernel used for calculating the estimate of the spectrum.
nsim	number of model simulations to be computed.
seed	optional; if non-NULL, the random number generator will be initialized with this seed for simulations. See simulate.
transform.data	function; this transformation will be applied to the observables prior to estimation of the spectrum, and prior to any detrending.
detrend	de-trending operation to perform. Options include no detrending, and subtraction of constant, linear, and quadratic trends from the data. Detrending is applied to each data series and to each model simulation independently.
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.
rmeasure	simulator of the measurement model, 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 rmeasure=NULL removes the measurement model simulator. For more information, see rmeasure specification.
...	additional arguments are passed to pomp.
verbose	logical; if TRUE, diagnostic messages will be printed to the console.

Details

spect estimates the power spectrum of time series data and model simulations and compares the results. It can be used to diagnose goodness of fit and/or as the basis for frequency-domain parameter estimation (spect.match).

A call to spect results in the estimation of the power spectrum for the (transformed, detrended) data and nsim model simulations. The results of these computations are stored in an object of class ‘spectd_pomp’.

When spect operates on a spectrum-matching objective function (a ‘spect_match_objfun’ object), by default, the random-number generator seed is fixed at the value given when the objective function was constructed. Specifying NULL or an integer for seed overrides this behavior.

Value

An object of class ‘spectd_pomp’, which contains the model, the data, and the results of the spect computation. The following methods are available:

plot

produces some diagnostic plots

summary

displays a summary

logLik

gives a measure of the agreement of the power spectra

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)

Daniel C. Reuman, Cai GoGwilt, Aaron A. King

References

\Reuman

2006

\Reuman

2008

See Also

More on methods based on summary statistics: abc(), basic_probes, nlf, probe(), probe_match, spect_match

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

pomp documentation built on Sept. 13, 2024, 1:08 a.m.