Description Usage Arguments Value Note See Also Examples
Calculate nonparametric estimates for the dependence function A of the multivariate extreme value distribution and plot the estimated function in the trivariate case.
1 2 3 4 
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 nonmissing
value is used in estimating the margins.
The dependence function of the multivariate extreme value
distribution is defined in amvevd
.
The function amvevd
calculates and plots dependence
functions of multivariate logistic and multivariate asymmetric
logistic models.
The estimator plotted or calculated is a multivariate extension of
the bivariate Pickands estimator defined in abvnonpar
.
1 2 3 4 5 6 7 8 9  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)

[1] 0.5977544 0.5804372 0.7155213 0.6422893 0.5682064 0.5757996 0.6631578
[8] 0.5996574 0.5981655 0.6740658
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