amvnonpar {evd} | R Documentation |
Calculate or plot non-parametric estimates for the dependence function A of the trivariate extreme value distribution.
amvnonpar(x = rep(1/3,3), data, epmar = FALSE, nsloc1 = NULL, nsloc2 = NULL, nsloc3 = NULL, madj = 0, 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 three or a matrix with three columns,
in which case the dependence function is evaluated across
the rows (ignored if plot is TRUE ). The elements/rows
of the vector/matrix should be positive and should sum to one,
or else they should have a positive sum, in which case the rows
are rescaled and a warning is given. A(1/3,1/3,1/3) is
returned by default since it is often a useful summary of
dependence. |
data |
A matrix or data frame with three columns, which may contain missing values. |
epmar |
If TRUE , an empirical transformation of the
marginals is performed in preference to marginal parametric
GEV estimation, and the nsloc arguments are ignored. |
nsloc1, nsloc2, nsloc3 |
A data frame with the same number of
rows as data , for linear modelling of the location
parameter on the first/second/third margin.
The data frames are treated as covariate matrices, excluding the
intercept.
A numeric vector can be given as an alternative to a single column
data frame. |
madj |
Performs marginal adjustments. See
abvnonpar . |
plot |
Logical; if TRUE the function is plotted. The
minimum (evaluated) value is returned invisibly.
If FALSE (the default), the following arguments are
ignored. |
col |
A list of colours (see image ). The first
colours in the list represent smaller values, and hence
stronger dependence. Each colour represents an equally spaced
interval between lower and one. |
blty |
The border line type, for the border that surrounds
the triangular image. By default blty is zero, so no
border is plotted. Plotting a border leads to (by default) an
increase in grid (and hence computation time), to ensure
that the image fits within it. |
grid |
For plotting, the function is evaluated at grid^2
points. |
lower |
The minimum value for which colours are plotted. By
default 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
ord . The argument alters the way in which the function
is plotted; it does not change the function definition. |
lab |
A character vector of length three, in which case the
i th margin is labelled using the i th component,
or NULL , in which case no labels are given. By default,
lab is as.character(1:3) . The actual location of
the margins, and hence the labels, is defined by ord . |
lcex |
A numerical value giving the amount by which the
labels should be scaled relative to the default. Ignored
if lab is NULL . |
amvnonpar
calculates or plots a non-parametric estimate of
the dependence function of the trivariate extreme value distribution.
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 trivariate extreme value
distribution is defined in amvevd
.
The function amvevd
calculates and plots dependence
functions of trivariate logistic and trivariate asymmetric
logistic models.
The estimator plotted or calculated is a trivariate extension of
the bivariate Pickands estimator defined in
abvnonpar
.
s3pts <- matrix(rexp(30), nrow = 10, ncol = 3) s3pts <- s3pts/rowSums(s3pts) sdat <- rmvevd(100, dep = 0.6, model = "log", d = 3) amvnonpar(s3pts, sdat) ## 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)