gausstrvineMST: Gaussian truncated d-dimensional vines based on sequential...
In YafeiXu/CopulaModel: CopulaModel: Dependence Modeling with Copulas

Description Usage Arguments Details Value See Also Examples

Gaussian truncated d-dimensional vines based on sequential minimum spanning trees with weights of one minus squared partial correlation

1	gausstrvine.mst(rmat,ntrunc,iprint=F)

`rmat`	dxd correlation matrix
`ntrunc`	specified upper bound in truncation level to consider
`iprint`	print flag for intermediate results

This function depends on the minimum spanning tree algorithm in the library igraph0.

`RVM`	object with $RVM$VineA = d-dimensional vine array, $RVM$pc = partial correlations by tree, $RVM$Matrix = vine array in VineCopula format [d:1,d:1], $RVM$Cor = partial correlations in VineCopula format [d:1,d:1]
`mst`	spanning trees 1,2,...d-1: $mst[[1]], $mst[[2]], ...
`treeweight`	vector of length d-1 with sum_edge log(1-rho[edge]^2) for trees 1,...d-1
`trunclevel`	same as inputted ntrunc
`truncval`	sum treeweight[1:trunclevel] / sum treeweight[1:(d-1)]

gausstrvine

## Not run: 
d=5
library(igraph0) # version 0.5.6 works
rmat=matrix(c(
1.00,0.76,0.76,0.74,0.67,
0.76,1.00,0.91,0.93,0.86,
0.76,0.91,1.00,0.94,0.85,
0.74,0.93,0.94,1.00,0.88,
0.67,0.86,0.85,0.88,1.00), d,d)
colnames(rmat) = rownames(rmat) = paste("V",1:d,sep="")
out=gausstrvine.mst(rmat,ntrunc=3,iprint=TRUE)
print(out) 
## End(Not run)