basic_probes | R Documentation |
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)
var, vars |
character; the name(s) of the observed variable(s). |
trim |
the fraction of observations to be trimmed (see |
transform |
transformation to be applied to the data before the probe is computed. |
na.rm |
if |
kernel.width |
width of modified Daniell smoothing kernel to be used
in power-spectrum computation: see |
probs |
the quantile or quantiles to compute: see |
... |
additional arguments passed to the underlying algorithms. |
lags |
In In |
type |
Compute autocorrelation or autocovariance? |
ref |
empirical reference distribution. Simulated data will be
regressed against the values of |
order |
order of polynomial regression. |
diff |
order of differencing to perform. |
powers |
the powers of each term (corresponding to |
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.
Daniel C. Reuman, Aaron A. King
1999
\Wood2010
More on methods based on summary statistics:
abc()
,
nlf
,
probe_match
,
probe()
,
spect_match
,
spect()
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