R: Methods for objects of class '"kgaps"'

kgaps_methods {exdex}

R Documentation

Methods for objects of class `"kgaps"`

Description

Methods for objects of class c("kgaps", "exdex") returned from kgaps.

Usage

## S3 method for class 'kgaps'
coef(object, ...)

## S3 method for class 'kgaps'
vcov(object, type = c("observed", "expected"), ...)

## S3 method for class 'kgaps'
nobs(object, ...)

## S3 method for class 'kgaps'
logLik(object, ...)

## S3 method for class 'kgaps'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'kgaps'
summary(
  object,
  se_type = c("observed", "expected"),
  digits = max(3, getOption("digits") - 3L),
  ...
)

## S3 method for class '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.