Non-parametric Estimates for Dependence Functions of the Multivariate Extreme Value Distribution
Calculate non-parametric estimates for the dependence function A of the multivariate extreme value distribution and plot the estimated function in the trivariate case.
amvnonpar(x = rep(1/d,d), data, d = 3, epmar = FALSE, nsloc = NULL, madj = 0, kmar = NULL, plot = FALSE, col = heat.colors(12), blty = 0, grid = if(blty) 150 else 50, lower = 1/3, ord = 1:3, lab = as.character(1:3), lcex = 1)
x |
A vector of length |
data |
A matrix or data frame with |
d |
The dimension; an integer greater than or equal to two.
The trivariate case |
epmar |
If |
nsloc |
A data frame with the same number of rows as |
madj |
Performs marginal adjustments. See
|
kmar |
In the rare case that the marginal distributions are known, specifies the GEV parameters to be used instead of maximum likelihood estimates. |
plot |
Logical; if |
col |
A list of colours (see |
blty |
The border line type, for the border that surrounds
the triangular image. By default |
grid |
For plotting, the function is evaluated at |
lower |
The minimum value for which colours are plotted. By default \code{lower} = 1/3 as this is the theoretical minimum of the dependence function of the trivariate extreme value distribution. |
ord |
A vector of length three, which should be a permutation
of the set {1,2,3}. The points (1,0,0),
(0,1,0) and (0,0,1) (the vertices of the simplex)
are depicted clockwise from the top in the order defined by
|
lab |
A character vector of length three, in which case the
|
lcex |
A numerical value giving the amount by which the
labels should be scaled relative to the default. Ignored
if |
A numeric vector of estimates. If plotting, the smallest evaluated estimate is returned invisibly.
The rows of data
that contain missing values are not used
in the estimation of the dependence structure, but every non-missing
value is used in estimating the margins.
The estimator plotted or calculated is a multivariate extension of
the bivariate Pickands estimator defined in abvnonpar
.
s5pts <- matrix(rexp(50), nrow = 10, ncol = 5) s5pts <- s5pts/rowSums(s5pts) sdat <- rmvevd(100, dep = 0.6, model = "log", d = 5) amvnonpar(s5pts, sdat, d = 5) ## Not run: amvnonpar(data = sdat, plot = TRUE) ## Not run: amvnonpar(data = sdat, plot = TRUE, ord = c(2,3,1), lab = LETTERS[1:3]) ## Not run: amvevd(dep = 0.6, model = "log", plot = TRUE) ## Not run: amvevd(dep = 0.6, model = "log", plot = TRUE, blty = 1)
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