gfigpd1 | R Documentation |
Runs the MCMC sampler of the fiducial distribution for the generalized Pareto model with known threshold.
gfigpd1(
X,
beta,
threshold,
gamma.init = NA,
sigma.init = NA,
sd.gamma = NA,
sd.sigma = NA,
Jnumb = 50L,
iter = 10000L,
burnin = 2000L,
thin = 6L,
nchains = 4L,
nthreads = parallel::detectCores(),
seeds = NULL
)
X |
numeric vector of data |
beta |
vector of probabilities corresponding to the quantiles to be estimated |
threshold |
value of the known threshold, must be smaller than the
maximum of |
gamma.init |
starting value for |
sigma.init |
starting value for |
sd.gamma |
standard deviation for the proposed |
sd.sigma |
standard deviation for the proposed |
Jnumb |
number of subsamples that are taken from the Jacobian |
iter |
number of iterations per chain (burnin excluded) |
burnin |
number of the first MCMC iterations discarded |
thin |
thinning number for the MCMC chain. (e.g. if it is 1 no iteration is skipped) |
nchains |
number of MCMC chains to run |
nthreads |
number of threads to run the chains in parallel |
seeds |
the seeds used for the MCMC sampler; one seed per chain, or
|
An object of class mcmc
if nchains=1
,
otherwise an object of class mcmc.list
.
Damian V. Wandler & Jan Hannig. Generalized fiducial confidence intervals for extremes. Extremes (2012) 15:67–87. <doi:10.1007/s10687-011-0127-9>
set.seed(666L)
X <- rgpareto(200L, mu = 10, gamma = 0.5, sigma = 1)
gf <- gfigpd1(
X, beta = c(0.98, 0.99), threshold = 10,
iter = 2000L, nchains = 2L, nthreads = 2L
) # note: 2*2000 iterations is not enough, I'm using these settings because
# of CRAN constraints (elapsed time must be < 5s)
summary(gf)
qgpareto(c(0.98, 0.99), mu = 10, gamma = 0.5, sigma = 1)
rejectionRate(gf)
HPDinterval(gf)
HPDinterval(joinMCMCchains(gf))
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