angular | R Documentation |
Empirical estimation of the Pickands dependence function, the angular density, the angular measure, and random generation of samples from the estimated angular density.
angular(data, model, n, dep, asy, alpha, beta, df, seed, k, nsim,
plot = TRUE, nw = 100)
data |
The dataset in vector form. |
model |
A character string specifying the model. Must be one of:
|
n |
The number of random generations from the |
dep |
The dependence parameter for the |
asy |
A vector of length two for asymmetry parameters, required for
asymmetric logistic ( |
alpha , beta |
Parameters for the bilogistic, negative bilogistic, Coles-Tawn, and asymmetric mixed models. |
df |
The degrees of freedom for the Extremal-t model. |
seed |
Seed for data generation. Required if |
k |
The polynomial order. |
nsim |
The number of generations from the estimated angular density. |
plot |
Logical; if |
nw |
The number of points at which the estimated functions are evaluated. |
See Marcon et al. (2017) for details.
A list containing:
The specified model.
Number of random generations.
Dependence parameter.
Input dataset.
Estimated Pickands dependence function.
Estimated angular density.
Estimated angular measure.
Point masses at the edge of the simplex.
Simulated sample from the angular density.
True Pickands dependence function and angular density,
if model
is specified.
Simone Padoan, simone.padoan@unibocconi.it, https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com, https://www.borisberanger.com; Giulia Marcon, giuliamarcongm@gmail.com
Marcon, G., Naveau, P. and Padoan, S. A. (2017). A semi-parametric stochastic generator for bivariate extreme events, Stat 6(1), 184–201.
################################################
# The following examples correspond to left panels
# of Figures 1, 2 & 3 from Marcon et al. (2017)
################################################
## Figure 1 - symmetric logistic
# Strong dependence
a <- angular(model = 'log', n = 50, dep = 0.3,
seed = 4321, k = 20, nsim = 10000)
# Mild dependence
b <- angular(model = 'log', n = 50, dep = 0.6,
seed = 212, k = 10, nsim = 10000)
# Weak dependence
c <- angular(model = 'log', n = 50, dep = 0.9,
seed = 4334, k = 6, nsim = 10000)
## Figure 2 - asymmetric logistic
# Strong dependence
d <- angular(model = 'alog', n = 25, dep = 0.3,
asy = c(0.3,0.8), seed = 43121465, k = 20, nsim = 10000)
# Mild dependence
e <- angular(model = 'alog', n = 25, dep = 0.6,
asy = c(0.3,0.8), seed = 1890, k = 10, nsim = 10000)
# Weak dependence
f <- angular(model = 'alog', n = 25, dep = 0.9,
asy = c(0.3,0.8), seed = 2043, k = 5, nsim = 10000)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.