summary.bbeed {ExtremalDep} | R Documentation |
Summary statistics of the MCMC output obtained from the Bayesian method based on the Bernstein polynomials for inferring the angular measure and Pickands dependence functions.
## S3 method for class 'bbeed'
summary(object, mcmc, burn, conf=0.95, plot=FALSE, ...)
object |
The values (on the simplex) at which the dependence is evaluated |
mcmc |
The output of an MCMC algorithm given by |
burn |
The burn-in period |
conf |
The confidence region |
plot |
If |
... |
Arguments to be passed for the graphical parameters |
Returns a list that contains:
* the conf
-, 0.5- and 1-conf
-quantiles and posterior sample for k (the polynomial order),
* the conf
- and 1-conf
-quantiles, mean and posterior sample for h (the angular density), A (the Pickands dependence function), p0 and p1 (the point masses at the endpoints of the simplex), mar1 and mar2 (the marginal parameters, if they exist). To access them, the names are for example k.low
, k.median
, k.up
and k_post
.
* w
and burn
which are the inputs object
and burn
.
Simone Padoan, simone.padoan@unibocconi.it, https://mypage.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com/; Giulia Marcon, giuliamarcongm@gmail.com
Marcon G., Padoan, S.A. and Antoniano-Villalobos I. (2016) Bayesian Inference for the Extremal Dependence. Electronic Journal of Statistics, 10.2, 3310-3337.
if (interactive()){
# This reproduces some of the results showed in Fig. 1 (Marcon, 2016).
set.seed(1890)
data <- evd::rbvevd(n=100, dep=.6, asy=c(0.8,0.3), model="alog", mar1=c(1,1,1))
nsim = 500000
burn = 400000
mu.nbinom = 3.2
var.nbinom = 4.48
hyperparam <- list(a.unif=0, b.unif=.5, mu.nbinom=mu.nbinom, var.nbinom=var.nbinom)
k0 = 5
pm0 = list(p0=0.06573614, p1=0.3752118)
eta0 = ExtremalDep:::rcoef(k0, pm0)
mcmc <- bbeed(data, pm0, eta0, k0, hyperparam, nsim,
prior.k = "nbinom", prior.pm = "unif")
w <- seq(0.001, .999, length=100)
summary.mcmc <- summary.bbeed(w, mcmc, burn, nsim, plot=TRUE)
}