Methods for objects of class "kgaps"

Source: R/kgaps_methods.R

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, ...)

Arguments

object

and object of class c("kgaps", "exdex") returned from kgaps.

...

For print.summary.kgaps, additional arguments passed to print.default.

type

A character scalar. Should the estimate of the variance be based on the observed information or the expected information?

x

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.

digits

print.kgaps. The argument digits to print.default.

summary.kgaps. An integer. Used for number formatting with signif.

se_type

A character scalar. Should the estimate of the standard error be based on the observed information or the expected information?

Value

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.

Examples

See the examples in kgaps.

See also

kgaps for maximum likelihood estimation of the extremal index \(\theta\) using the \(K\)-gaps model.

confint.kgaps for confidence intervals for \(\theta\).