liouv.maxim | R Documentation |
Two methods, either numerical optimization or method-of-moments
liouv.maxim(
data,
family,
interval,
boundary = NULL,
lattice.mat = NULL,
return_all = FALSE,
MC.approx = TRUE
)
data |
sample matrix from a Liouville copula |
family |
family of the Liouville copula. Either |
interval |
interval over which to look for |
boundary |
vector of endpoints for search of Dirichlet allocation parameters. Either |
lattice.mat |
matrix of tuples of Dirichlet allocation parameters at which to evaluate the likelihood |
return_all |
should all results (as list) or only maximum value be returned. Defaults to |
MC.approx |
whether to use Monte-Carlo approximation for the inverse survival function (default is |
A wrapper to optim
using the Nelder-Mead algorithm or using the methods of moments,
to maxime pointwise given every alphavec
over a grid.
Returns the maximum for alphavec
and theta
.
a list with values of theta
and Dirichlet parameter along with maximum found. Gives index of maximum amongst models fitted.
## Not run:
data <- rliouv(n=100, family="joe", alphavec=c(1,2), theta=2)
liouv.maxim(data=data, family="j", interval=c(1.25,3), boundary=c(2,2),return_all=TRUE)
lattice.mat <- t(combn(1:3,2))
liouv.maxim(data=data, family="j", interval=c(1.25,3), lattice.mat=lattice.mat, return_all=FALSE)
#data <- rliouv(n=1000, family="gumbel", alphavec=c(1,2), theta=2)
liouv.maxim.mm(data=data, family="gumbel", boundary=c(3,3),return_all=TRUE)
lattice.mat <- t(combn(1:3,2))
liouv.maxim.mm(data=data, family="gumbel", lattice.mat=lattice.mat, return_all=FALSE)
## End(Not run)
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