S3 `alogLik`

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

, `fit.gpd`

, and
`fit.pp`

and `fit.rlarg`

in the
mev package.

```
# S3 method for mev_gev
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)
# S3 method for mev_pp
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)
# S3 method for mev_gpd
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)
# S3 method for mev_egp
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)
# S3 method for mev_rlarg
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)
```

- 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", "mev")`

.
The 4th component depends on which model was fitted.

`"gev"`

if `fit.gev`

was used;

`"gpd"`

if `fit.gpd`

was used;

`"pp"`

`fit.pp`

was used;

`"egp"`

`fit.egp`

was used;

`"rlarg"`

`fit.rlarg`

was used;
The 5th component is `"stat"`

(for stationary).

See `alogLik`

for details.

If `x`

was returned from `fit.pp`

then the data
`xdat`

supplied to `fit.pp`

must contain *all*
the data, both threshold exceedances and non-exceedances.

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 mev package
got_mev <- requireNamespace("mev", quietly = TRUE)
if (got_mev) {
library(mev)
# An example from the mev::gev.fit documentation
gev_mev <- fit.gev(revdbayes::portpirie)
adj_gev_mev <- alogLik(gev_mev)
summary(adj_gev_mev)
# Use simulated data
set.seed(1112019)
x <- revdbayes::rgp(365 * 10, loc = 0, scale = 1, shape = 0.1)
pfit <- fit.pp(x, threshold = 1, npp = 365)
adj_pfit <- alogLik(pfit)
summary(adj_pfit)
# An example from the mev::fit.gpd documentation
gpd_mev <- fit.gpd(eskrain, threshold = 35, method = 'Grimshaw')
adj_gpd_mev <- alogLik(gpd_mev)
summary(adj_gpd_mev)
# An example from the mev::fit.egp documentation
# (model = "egp1" and model = "egp3" also work)
xdat <- evd::rgpd(n = 100, loc = 0, scale = 1, shape = 0.5)
fitted <- fit.egp(xdat = xdat, thresh = 1, model = "egp2", show = FALSE)
adj_fitted <- alogLik(fitted)
summary(adj_fitted)
# An example from the mev::fit.rlarg documentation
set.seed(31102019)
xdat <- rrlarg(n = 10, loc = 0, scale = 1, shape = 0.1, r = 4)
fitr <- fit.rlarg(xdat)
adj_fitr <- alogLik(fitr)
summary(adj_fitr)
}
#>
#> Attaching package: 'mev'
#> The following object is masked _by_ '.GlobalEnv':
#>
#> venice
#> The following objects are masked from 'package:fExtremes':
#>
#> dgev, pgev, qgev, rgev
#> The following objects are masked from 'package:evir':
#>
#> dgev, pgev, qgev, rgev
#> The following objects are masked from 'package:eva':
#>
#> pgev, qgev
#> The following object is masked from 'package:extRemes':
#>
#> taildep
#> The following objects are masked from 'package:evd':
#>
#> dgev, pgev, qgev, rgev, venice
#> MLE SE adj. SE
#> loc -0.1223 0.2859 0.29490
#> scale 1.0750 0.2450 0.20520
#> shape 0.2490 0.1552 0.07926
```