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, ...)



A fitted model object with certain associated S3 methods. See Details.


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.


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

See also

alogLik: loglikelihood adjustment for model fits.


# We need the evd package
got_evd <- requireNamespace("evd", quietly = TRUE)

if (got_evd) {
  # An example from the evd::fgev documentation
  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)

  # An example from Chandler and Bate (2007)
  owfit <- fgev(ow$temp, nsloc = ow$loc)
  adj_owfit <- alogLik(owfit, cluster = ow$year)

  # An example from the evd::fpot documentation
  uvdata <- evd::rgpd(100, loc = 0, scale = 1.1, shape = 0.2)
  M1 <- fpot(uvdata, 1)
  adj_fpot <- alogLik(M1)
  # Fit using the pp model, rather than the gpd
  M1 <- fpot(uvdata, 1, model = "pp", npp = 365)
  adj_fpot <- alogLik(M1)
#>           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