S3 `alogLik`

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

and `fpot`

in the evd package.
If `x`

is returned from `fgev`

then the call must
have used `prob = NULL`

.

```
# S3 method for evd
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", "evd")`

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

was used then these components are

`c("gev", "stat")`

if `nsloc`

was `NULL`

and

`c("gev", "nonstat")`

if `nsloc`

was not `NULL`

.
If `fpot`

was used then these components are

`c("pot", "gpd")`

if `model`

was `"gpd"`

and

`c("pot", "pp")`

if `model`

was `"pp"`

.

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 evd package
got_evd <- requireNamespace("evd", quietly = TRUE)
if (got_evd) {
library(evd)
# An example from the evd::fgev documentation
set.seed(3082019)
uvdata <- evd::rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
M1 <- evd::fgev(uvdata, nsloc = (-49:50)/100)
adj_fgev <- alogLik(M1)
summary(adj_fgev)
# An example from Chandler and Bate (2007)
owfit <- fgev(ow$temp, nsloc = ow$loc)
adj_owfit <- alogLik(owfit, cluster = ow$year)
summary(adj_owfit)
# An example from the evd::fpot documentation
set.seed(3082019)
uvdata <- evd::rgpd(100, loc = 0, scale = 1.1, shape = 0.2)
M1 <- fpot(uvdata, 1)
adj_fpot <- alogLik(M1)
summary(adj_fpot)
# Fit using the pp model, rather than the gpd
M1 <- fpot(uvdata, 1, model = "pp", npp = 365)
adj_fpot <- alogLik(M1)
summary(adj_fpot)
}
#> MLE SE adj. SE
#> loc 13.6100 5.6170 3.9330
#> scale 3.8420 3.2740 2.4440
#> shape 0.1913 0.1964 0.1617
```