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.marginal(var, ref, order = 3, diff = 1, transform = identity)
probe.nlar(var, lags, powers, transform = identity)
probe.acf(var, lags, type = c("covariance", "correlation"), transform = identity)
probe.ccf(vars, lags, type = c("covariance", "correlation"), transform = identity)
probe.period(var, kernel.width, transform = identity)
probe.quantile(var, prob, transform = identity)
mean
).
TRUE
, remove all NA observations prior to computing the probe.
kernel
.
quantile
.
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.
lags
) in the the nonlinear autoregressive model that will be fit to the actual and simulated data.
See Details, below, for a precise description.
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.
probe
or probe.match
.
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.
S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems, Nature, 466: 1102--1104, 2010.