basic_probes: Useful probes for partially-observed Markov processes

In pomp: Statistical Inference for Partially Observed Markov Processes

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

\Kendall

1999

\Wood

2010

See Also

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

pomp documentation built on June 8, 2025, 10:20 a.m.