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.
probe.mean, probe.median, probe.var, probe.sdvar, respectively.
probe.periodvar series with largest power.
probe.marginalvar against the reference distribution ref.
If diff>0, the data and the reference distribution are first differenced diff times and centered.
Polynomial regression of order order is used.
This probe returns order regression coefficients (the intercept is zero).
probe.nlarvar).
Specifically, a model of the form $y[t] = \sum beta[k] y[t-tau[k]]^p[k]+e[t]$ will be fit, where $tau[k]$ are the lags and $p[k]$ are the powers.
The data are first centered.
This function returns the regression coefficients, $beta[k]$.
probe.acftype=="covariance", computes the autocovariance of variable var at lags lags;
if type=="correlation", computes the autocorrelation of variable var at lags lags.
probe.ccftype=="covariance", computes the cross covariance of the two variables named in vars at lags lags;
if type=="correlation", computes the cross correlation.
probe.quantileprob-th quantile of variable var.
S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems, Nature, 466: 1102--1104, 2010.