Description Usage Arguments Details Value References See Also Examples
Loglikelihood adjustment for fitting Generalised Extreme Value (GEV) model and
Threshold Modelling using generalised Pareto distribution (GPD). The adjustment is
based on package ismev
.
1 2 3 4 5 |
x |
A fitted model object. |
cluster |
see details in |
use_vcov |
A logical scalar. If |
... |
Further arguments to be passed to the functions in the
sandwich package |
The fitted object must have S3 methods:
logLikVec
, coef
, and nobs
. It may have method vcov
and
estfun
. If a vcov
method is not available then the variance-covariance
matrix of the model parameters is estimated inside
adjust_loglik
. If an estfun
method is not available
then the score matrix is estimated using jacobian
. More in
See Also.
An object of class c("oolax", "chandwich")
with the same structure as an
object returned from adjust_loglik
.
Zeileis A (2006). <e2><80><9c>Object-Oriented Computation of Sandwich Estimators.<e2><80><9d> _Journal of Statistical Software_, *16*(9), 1-16. doi: 10.18637/jss.v016.i09 (URL:http://doi.org/10.18637/jss.v016.i09).
adjust_loglik
Loglikelihood adjustment using the sandwich
estimator.
logLikVec.gev
Loglikelihood vector for GEV Distribution fitted
by fgev
logLikVec.pot
Loglikelihood vector for GPD Distribution fitted
by fpot
logLikVec.ismev_gev
Loglikelihood vector for GEV Distribution
fitted by gev.fit
logLikVec.ismev_gpd
Loglikelihood vector for GPD Distribution
fitted by gpd.fit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # We need the ismev package
got_ismev <- requireNamespace("ismev", quietly = TRUE)
if (got_ismev) {
library(ismev)
# An example from Chandler and Bate (2007)
y <- c(chandwich::owtemps[, "Oxford"], chandwich::owtemps[, "Worthing"])
x <- as.matrix(rep(c(1, -1), each = length(y) / 2))
owfit <- oogev.fit(y, x, mul = 1, sigl = 1, shl = 1, method = "BFGS" )
year <- rep(1:(length(y) / 2), 2)
adj_owfit <- alogLik(owfit, cluster = year, cadjust = FALSE)
summary(adj_owfit)
# An example from the gpd.fit() documentation
data(rain)
fit <- oogpd.fit(rain, 10)
adj_fit <- alogLik(fit)
summary(adj_fit)
}
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