gev.tem {mev} | R Documentation |
Tangent exponential model approximation for the GEV distribution
Description
The function gev.tem
provides a tangent exponential model (TEM) approximation
for higher order likelihood inference for a scalar parameter for the generalized extreme value distribution.
Options include location scale and shape parameters as well as value-at-risk (or return levels).
The function attempts to find good values for psi
that will
cover the range of options, but the fail may fit and return an error.
Usage
gev.tem(
param = c("loc", "scale", "shape", "quant", "Nmean", "Nquant"),
dat,
psi = NULL,
p = NULL,
q = 0.5,
N = NULL,
n.psi = 50,
plot = TRUE,
correction = TRUE
)
Arguments
param |
parameter over which to profile |
dat |
sample vector for the GEV distribution |
psi |
scalar or ordered vector of values for the interest parameter. If |
p |
tail probability for the (1-p)th quantile (return levels). Required only if |
q |
probability level of quantile. Required only for |
N |
size of block over which to take maxima. Required only for |
n.psi |
number of values of |
plot |
logical indicating whether |
correction |
logical indicating whether spline.corr should be called. |
Value
an invisible object of class fr
(see tem
in package hoa
) with elements
-
normal
: maximum likelihood estimate and standard error of the interest parameter\psi
-
par.hat
: maximum likelihood estimates -
par.hat.se
: standard errors of maximum likelihood estimates -
th.rest
: estimated maximum profile likelihood at (\psi
,\hat{\lambda}
) -
r
: values of likelihood root corresponding to\psi
-
psi
: vector of interest parameter -
q
: vector of likelihood modifications -
rstar
: modified likelihood root vector -
rstar.old
: uncorrected modified likelihood root vector -
param
: parameter
Author(s)
Leo Belzile
Examples
## Not run:
set.seed(1234)
dat <- rgev(n = 40, loc = 0, scale = 2, shape = -0.1)
gev.tem('shape', dat = dat, plot = TRUE)
gev.tem('quant', dat = dat, p = 0.01, plot = TRUE)
gev.tem('scale', psi = seq(1, 4, by = 0.1), dat = dat, plot = TRUE)
dat <- rgev(n = 40, loc = 0, scale = 2, shape = 0.2)
gev.tem('loc', dat = dat, plot = TRUE)
gev.tem('Nmean', dat = dat, p = 0.01, N=100, plot = TRUE)
gev.tem('Nquant', dat = dat, q = 0.5, N=100, plot = TRUE)
## End(Not run)