basic_probes function

Useful probes for partially-observed Markov processes

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

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.

Returns

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.

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. tools:::Rd_expr_doi("10.2307/176658") .

S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466 , 1102--1104, 2010. tools:::Rd_expr_doi("10.1038/nature09319") .

See Also

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

Author(s)

Daniel C. Reuman, Aaron A. King