ddirimix {BMAmevt} | R Documentation |
Likelihood function (spectral density on the simplex) and angular data sampler in the Dirichlet mixture model.
ddirimix(x = c(0.1, 0.2, 0.7), par, wei = par$wei, Mu = par$Mu, lnu = par$lnu, log = FALSE, vectorial = FALSE) rdirimix(n = 10, par = get("dm.expar.D3k3"), wei = par$wei, Mu = par$Mu, lnu = par$lnu)
x |
An angular data set which may be reduced to a single point:
A n*p matrix or a vector of length |
par |
The parameter list for the Dirichlet mixture model. |
wei |
Optional. If present, overrides the value of
|
Mu |
Optional. If present, overrides the value of
|
lnu |
Optional. If present, overrides the value of
|
log |
Logical: should the density or the likelihood be returned on the log-scale ? |
vectorial |
Logical: Should a vector of size n or a single value be returned ? |
n |
The number of angular points to be generated |
The spectral probability measure defined on the simplex characterizes the dependence structure of multivariate extreme value models. The parameter list for a mixture with k components, is made of
The density kernel centers
μ[1:p,1:k] :
A p*k matrix,
which columns sum to one, and such that Mu %*% wei=1
,
for the moments constraint to be satisfied.
Each column is a Dirichlet kernel center.
The weights vector for the kernel densities: A vector of k positive numbers summing to one.
The logarithms of the shape parameters ν[1:k] for the density kernels: a vector of size k.
The moments constraint imposes that the barycenter of the columns in
Mu
, with weights wei
, be the center of the simplex.
ddirimix
returns the likelihood as a single number if
vectorial ==FALSE
, or as a vector of size
nrow(x)
containing the likelihood of each angular data point.
If log == TRUE
, the log-likelihood is returned instead.
rdirimix
returns a matrix with n
points and
p=nrow(Mu)
columns.