S3 `alogLik`

method to perform loglikelihood adjustment for fitted
extreme value model objects returned from the functions
`gevFit`

,
`gumbelFit`

and
`gpdFit`

in the `fExtremes`

package.
The model must have been fitted using maximum likelihood estimation.

- x
A fitted model object with certain associated S3 methods. See

**Details**.- cluster
A vector or factor indicating from which cluster the respective log-likelihood contributions from

`loglik`

originate. The length of`cluster`

must be consistent with the`estfun`

method to be used in the estimation of the 'meat'`V`

of the sandwich estimator of the covariance matrix of the parameters to be passed to`adjust_loglik`

. In most cases,`cluster`

must have length equal to the number of observations in data. The exception is the GP (only) model (`binom = FALSE`

), where the`cluster`

may either contain a value for each observation in the raw data, or for each threshold exceedance in the data.If

`cluster`

is not supplied (is`NULL`

) then it is assumed that each observation forms its own cluster. See**Details**for further details.- use_vcov
A logical scalar. Should we use the

`vcov`

S3 method for`x`

(if this exists) to estimate the Hessian of the independence loglikelihood to be passed as the argument`H`

to`adjust_loglik`

? Otherwise,`H`

is estimated inside`adjust_loglik`

using`optimHess`

.- ...
Further arguments to be passed to the functions in the sandwich package

`meat`

(if`cluster = NULL`

), or`meatCL`

(if`cluster`

is not`NULL`

).

An object inheriting from class `"chandwich"`

. See

`class(x)`

is a vector of length 5. The first 3 components are

`c("lax", "chandwich", "fExtremes")`

.
The remaining 2 components depend on the model that was fitted.
If `gevFit`

or

`gumbelFit`

was used then these
components are `c("gev", "stat")`

.
If `gpdFit`

was used then these
components are `c("gpd", "stat")`

.

See `alogLik`

for details.

Chandler, R. E. and Bate, S. (2007). Inference for clustered
data using the independence loglikelihood. *Biometrika*,
**94**(1), 167-183. doi:10.1093/biomet/asm015

Suveges, M. and Davison, A. C. (2010) Model
misspecification in peaks over threshold analysis, *The Annals of
Applied Statistics*, **4**(1), 203-221.
doi:10.1214/09-AOAS292

Zeileis (2006) Object-Oriented Computation and Sandwich
Estimators. *Journal of Statistical Software*, **16**, 1-16.
doi:10.18637/jss.v016.i09

`alogLik`

: loglikelihood adjustment for model fits.

```
# We need the fExtremes package
got_fExtremes <- requireNamespace("fExtremes", quietly = TRUE)
if (got_fExtremes) {
library(fExtremes)
# GEV
# An example from the fExtremes::gevFit documentation
set.seed(4082019)
x <- fExtremes::gevSim(model = list(xi=0.25, mu=0, beta=1), n = 1000)
# Fit GEV distribution by maximum likelihood estimation
fit <- fExtremes::gevFit(x)
adj_fit <- alogLik(fit)
summary(adj_fit)
# GP
# An example from the fExtremes::gpdFit documentation
# Simulate GP data
x <- fExtremes::gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000)
# Fit GP distribution by maximum likelihood estimation
fit <- fExtremes::gpdFit(x, u = min(x))
adj_fit <- alogLik(fit)
summary(adj_fit)
}
#>
#> Attaching package: 'fExtremes'
#> The following objects are masked from 'package:evir':
#>
#> dgev, dgpd, pgev, pgpd, qgev, qgpd, rgev, rgpd
#> The following objects are masked from 'package:eva':
#>
#> dgpd, gpdFit, mrlPlot, pgev, pgpd, qgev, qgpd, rgpd
#> The following objects are masked from 'package:evd':
#>
#> dgev, dgpd, pgev, pgpd, qgev, qgpd, rgev, rgpd
#> MLE SE adj. SE
#> xi 0.2514 0.03844 0.04026
#> beta 0.9989 0.04909 0.04852
```