Description Usage Arguments Value References Examples
Runs the MCMC sampler of the fiducial distribution for the generalized Pareto model with unknown threshold.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
X |
numeric vector of data |
beta |
vector of probabilities corresponding to the quantiles to be estimated |
threshold.init |
a guess of the unknown threshold, must be in the range
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 |
p1 |
probability that the MCMC will propose a new |
p2 |
probability that the new index proposed will be larger than the current index |
lambda1 |
the small jump the index variable will make |
lambda2 |
the large jump the index variable will make; happens 1 of every 10 iterations |
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
|
allParameters |
logical, whether to return the MCMC chains of all parameters (pretty useless) or only the ones of the quantiles |
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>
1 2 3 4 5 6 7 8 9 10 11 | set.seed(31415L)
X <- rgamma(350L, shape = 10, rate = 1)
gf <- gfigpd2(X, beta = c(0.98, 0.99), iter = 3000L, nthreads = 2L)
summary(gf)
qgamma(c(0.98, 0.99), shape = 10, rate = 1)
traceplot(gf[,"beta1"])
traceplot(gf[,"beta2"])
thresholdEstimate(gf)
rejectionRate(gf)
HPDinterval(gf)
HPDinterval(joinMCMCchains(gf))
|
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