plot.bbeed {ExtremalDep} | R Documentation |
Produces one or more plots of the extremal dependence.
## S3 method for class 'bbeed'
plot(x, type = c("summary", "returns", "A", "h", "pm", "k"),
mcmc, summary.mcmc, nsim, burn, y, probs, CEX=1.5, A_true, h_true,
labels=c(expression(y[1]),expression(y[2])), ...)
x |
Vector on the unit simplex where the dependence function is evaluated. |
type |
String, denoting the type of function to plot (see Details). |
mcmc |
The output of the bbeed function. |
summary.mcmc |
The output of the summary.bbeed function. |
nsim |
The number of simulation in the mcmc algorithm. |
burn |
The burn-in period. |
y |
A 2-column matrix of unobserved thresholds at which the returns are calculated. Required when |
probs |
The probability of joint exceedances, the output of the |
A_true |
The true pickands dependence function (evaluated at |
h_true |
The true angular density function (evaluated at |
CEX |
Label and axis sizes. |
labels |
Labels. |
... |
Additional graphical parameters. See |
If type="returns"
, a (contour) plot of the probabilities of exceedances for some threshold y
is returned.
If type="A"
, a plot of the estimated Pickands dependence function is drawn. If A_true
is specified the plot includes the true Pickands dependence function and a functional boxplot for the estimated function.
If type="h"
, a plot of the estimated angular density function is drawn. If h_true
is specified the plot includes the true angular density and a functional boxplot for the estimated function.
If type="pm"
, a plot of the prior against the posterior for the mass at \{0\}
is drawn.
If type="k"
, a plot of the prior against the posterior for the polynomial degree k
is drawn.
If type="summary"
, a 2 by 2 plot with types "A"
, "h"
, "pm"
and "k"
is returned.
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., Naveau, P., Muliere, P., Segers, J. (2017) Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials. Journal of Statistical Planning and Inference, 183, 1-17.
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)
plot.bbeed(type = "A", x=w, mcmc=mcmc, summary.mcmc, nsim=nsim, burn=burn)
plot.bbeed(type = "h", x=w, mcmc=mcmc, summary.mcmc, nsim=nsim, burn=burn)
plot.bbeed(type = "pm", x=w, mcmc=mcmc, summary.mcmc, nsim=nsim, burn=burn)
plot.bbeed(type = "k", x=w, mcmc=mcmc, summary.mcmc, nsim=nsim, burn=burn)
Atrue <- evd::abvevd(w, dep=0.6, asy=c(0.3,0.8), model='alog')
htrue <- evd::hbvevd(w, dep=0.6, asy=c(0.8,0.3), model='alog',half=TRUE)
plot.bbeed(type = "A", summary.mcmc=summary.mcmc, A_true=Atrue)
plot.bbeed(type = "h", summary.mcmc=summary.mcmc, h_true=htrue)
}