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)
character; the name(s) of the observed variable(s).
the fraction of observations to be trimmed (see mean).
transformation to be applied to the data before the probe is computed.
if TRUE, remove all NA observations prior to computing the probe.
width of modified Daniell smoothing kernel to be used
in power-spectrum computation: see kernel.
the quantile or quantiles to compute: see quantile.
additional arguments passed to the underlying algorithms.
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.
Compute autocorrelation or autocovariance?
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 of polynomial regression.
order of differencing to perform.
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.
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.
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.
Other summary statistics methods: abc,
probe.match, probe,
spect