R: Useful probes for partially-observed Markov processes

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.W. Murdoch, P. Turchin, S.P. Ellner, E. McCauley, R.M. Nisbet, and 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.