"kgaps"Methods for objects of class c("kgaps", "exdex") returned from
kgaps.
# S3 method for kgaps
coef(object, ...)# S3 method for kgaps
vcov(object, type = c("observed", "expected"), ...)
# S3 method for kgaps
nobs(object, ...)
# S3 method for kgaps
logLik(object, ...)
# S3 method for kgaps
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for kgaps
summary(
object,
se_type = c("observed", "expected"),
digits = max(3, getOption("digits") - 3L),
...
)
# S3 method for summary.kgaps
print(x, ...)
coef.kgaps. A numeric scalar: the estimate of the extremal index
\(\theta\).
vcov.kgaps. A \(1 \times 1\) numeric matrix containing the
estimated variance of the estimator.
nobs.kgaps. A numeric scalar: the number of inter-exceedance times
used in the fit. If x$inc_cens = TRUE then this includes up to 2
censored observations.
logLik.kgaps. An object of class "logLik": a numeric scalar
with value equal to the maximised log-likelihood. The returned object
also has attributes nobs, the numbers of \(K\)-gaps that
contribute to the log-likelihood and "df", which is equal to the
number of total number of parameters estimated (1).
print.kgaps. The argument x, invisibly.
summary.kgaps. Returns a list containing the list element
object$call and a numeric matrix summary giving the estimate
of the extremal index \(\theta\) and the estimated standard error
(Std. Error).
print.summary.kgaps. The argument x, invisibly.
and object of class c("kgaps", "exdex") returned from
kgaps.
For print.summary.kgaps, additional arguments passed to
print.default.
A character scalar. Should the estimate of the variance be based on the observed information or the expected information?
print.kgaps. An object of class c("kgaps", "exdex"), a
result of a call to kgaps.
print.summary.kgaps. An object of class "summary.kgaps", a
result of a call to summary.kgaps.
print.kgaps. The argument digits to
print.default.
summary.kgaps. An integer. Used for number formatting with
signif.
A character scalar. Should the estimate of the standard error be based on the observed information or the expected information?
See the examples in kgaps.
kgaps for maximum likelihood estimation of the
extremal index \(\theta\) using the \(K\)-gaps model.
confint.kgaps for confidence intervals for
\(\theta\).