basic_probes: Useful probes for partially-observed Markov processes

Description Usage Arguments Value Author(s) References See Also

Description

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

Usage

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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 pomp methods based on summary statistics: abc(), probe_matching, probe(), spect()


pomp documentation built on July 28, 2021, 5:10 p.m.