Simulation of first order Markov chains, such that each pair of consecutive values has the dependence structure of one of nine parametric bivariate extreme value distributions.
evmc(n, dep, asy = c(1,1), alpha, beta, model = c("log", "alog",
"hr", "neglog", "aneglog", "bilog", "negbilog", "ct", "amix"),
margins = c("uniform","rweibull","frechet","gumbel"))
A numeric vector of length n
.
Number of observations.
Dependence parameter for the logistic, asymmetric logistic, Husler-Reiss, negative logistic and asymmetric negative logistic models.
A vector of length two, containing the two asymmetry parameters for the asymmetric logistic and asymmetric negative logistic models.
Alpha and beta parameters for the bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models.
The specified model; a character string. Must be
either "log"
(the default), "alog"
, "hr"
,
"neglog"
, "aneglog"
, "bilog"
,
"negbilog"
, "ct"
or "amix"
(or any unique
partial match), for the logistic, asymmetric logistic,
Husler-Reiss, negative logistic, asymmetric negative logistic,
bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed
models respectively. The definition of each model is given in
rbvevd
. If parameter arguments are given that do
not correspond to the specified model those arguments are
ignored, with a warning.
The marginal distribution of each value; a
character string. Must be either "uniform"
(the
default), "rweibull"
, "frechet"
or
"gumbel"
(or any unique partial match), for the uniform,
standard reverse Weibull, standard Gumbel and standard Frechet
distributions respectively.
marma
, rbvevd
evmc(100, alpha = 0.1, beta = 0.1, model = "bilog")
evmc(100, dep = 10, model = "hr", margins = "gum")
Run the code above in your browser using DataLab