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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

θ.

vcov.kgaps. A 1×1 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 θ 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 θ using the K-gaps model.

confint.kgaps for confidence intervals for θ.