basic_probes {pomp} | R Documentation

## Useful probes for partially-observed Markov processes

### Description

Several simple and configurable probes are provided with in the package. These can be used directly and as templates for custom probes.

### Usage

    probe.mean(var, trim = 0, transform = identity, na.rm = TRUE)

    probe.median(var, na.rm = TRUE)

    probe.var(var, transform = identity, na.rm = TRUE)

    probe.sd(var, transform = identity, na.rm = TRUE)

    probe.period(var, kernel.width, transform = identity)

    probe.quantile(var, probs, ...)

    probe.acf(var, lags, type = c("covariance", "correlation"),
      transform = identity)

    probe.ccf(vars, lags, type = c("covariance", "correlation"),
      transform = identity)

    probe.marginal(var, ref, order = 3, diff = 1, transform = identity)

    probe.nlar(var, lags, powers, transform = identity)

### Arguments

`var, vars` | character; the name(s) of the observed variable(s).
`trim` | the fraction of observations to be trimmed (see `mean`).
`transform` | transformation to be applied to the data before the probe is computed.
`na.rm` | if `TRUE`, remove all NA observations prior to computing the probe.
`kernel.width` | width of modified Daniell smoothing kernel to be used in power-spectrum computation: see `kernel`.
`probs` | the quantile or quantiles to compute: see `quantile`.
`...` | additional arguments passed to the underlying algorithms.
`lags` | In `probe.ccf`, a vector of lags between time series. Positive lags correspond to `x` advanced relative to `y`; negative lags, to the reverse. In `probe.nlar`, a vector of lags present in the nonlinear autoregressive model that will be fit to the actual and simulated data. See Details, below, for a precise description.
`type` | Compute autocorrelation or autocovariance?
`ref` | empirical reference distribution. Simulated data will be regressed against the values of `ref`, sorted and, optionally, differenced. The resulting regression coefficients capture information about the shape of the marginal distribution. A good choice for `ref` is the data itself.
`order` | order of polynomial regression.
`diff` | order of differencing to perform.
`powers` | the powers of each term (corresponding to `lags`) in the the nonlinear autoregressive model that will be fit to the actual and simulated data. See Details, below, for a precise description.

### Value

A call to any one of these functions returns a probe function, suitable for use in `probe` or `probe_objfun`. That is, the function returned by each of these takes a data array (such as comes from a call to `obs`) as input and returns a single numerical value.

### Author(s)

Daniel C. Reuman, Aaron A. King

### References

B. E. Kendall, C. J. Briggs, W. M. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, S. N. Wood Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches, Ecology, 80:1789–1805, 1999.

S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems, Nature, 466: 1102–1104, 2010.

### See Also

Other summary statistics methods: `abc`, `probe.match`, `probe`, `spect`

[Package pomp version 2.3 Index]