amvnonpar | R Documentation |
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 |
ord |
A vector of length three, which should be a permutation
of the set |
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 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
.
amvevd
, abvnonpar
,
fgev
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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