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 logscale ? 
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 loglikelihood is returned instead.
rdirimix
returns a matrix with n
points and
p=nrow(Mu)
columns.