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.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)
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
.
a single probability; the quantile to compute: see quantile
.
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.
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.
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.
A call to any one of these functions returns a probe function, suitable for use in 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.
Each of these functions is relatively simple. See the source code for a complete understanding of what each does.
probe.mean
, probe.median
, probe.var
, probe.sd
return functions that compute the mean, median, variance, and standard deviation of variable var
, respectively.
probe.period
returns a function that estimates the period of the Fourier component of the var
series with largest power.
probe.marginal
returns a function that
regresses the marginal distribution of variable var
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.nlar
returns a function that
fit a nonlinear (polynomial) autoregressive model to the univariate series (variable var
).
Specifically, a model of the form \(y_t = \sum \beta_k y_{t-\tau_k}^{p_k}+\epsilon_t\) 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.acf
returns a function that,
if type=="covariance"
, computes the autocovariance of variable var
at lags lags
;
if type=="correlation"
, computes the autocorrelation of variable var
at lags lags
.
probe.ccf
returns a function that,
if type=="covariance"
, computes the cross covariance of the two variables named in vars
at lags lags
;
if type=="correlation"
, computes the cross correlation.
probe.quantile
returns a function that estimates the prob
-th quantile of variable var
.
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.