spect {pomp} | R Documentation |

## 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, |

`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- |

`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 |

`rinit` |
simulator of the initial-state distribution.
This can be furnished either as a C snippet, an |

`rprocess` |
simulator of the latent state process, specified using one of the rprocess plugins.
Setting |

`rmeasure` |
simulator of the measurement model, specified either as a C snippet, an |

`...` |
additional arguments are passed to |

`verbose` |
logical; if |

### 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

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. doi:10.1073/pnas.0608571103.

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. doi:10.1111/j.1461-0248.2008.01194.x.

### 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*version 5.11.0.0 Index]