spect: Power spectrum

Description

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

Usage

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

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_spec.

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_spec 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_spec.

...

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.

verbose

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

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

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.

References

D.C. Reuman, R.A. Desharnais, R.F. Costantino, O. Ahmad, J.E. Cohen. Power spectra reveal the influence of stochasticity on nonlinear population dynamics. Proceedings of the National Academy of Sciences 103, 18860-18865, 2006

D.C. Reuman, R.F. Costantino, R.A. Desharnais, J.E. Cohen. Color of environmental noise affects the nonlinear dynamics of cycling, stage-structured populations. Ecology Letters 11, 820-830, 2008.