Nothing
# General Reference:
# Joe H (2006). Generating random correlation matrices based on partial
# correlations. J. Mult. Anal. Vol. 97, 2177--2198.
# Random correlation with the C-vine (different order of partial
# correlations). Reference for vines: Kurowicka and Cooke, 2006,
# Uncertainty Analysis with High Dimensional Dependence Modelling,
# Wiley, 2006.
# qq plot against beta(alp,alp) distribution
qqbeta=function(x,alp)
{ xs=sort(x)
n=length(x)
pp=(1:n)/(n+1)
qq=qbeta(pp,alp,alp)
plot(qq,xs,ylab="corr",xlab="beta quantile")
title(paste("Beta quantile plot with a=b=",alp))
0
}
# input d>=2, eta>0 (eta=1 for uniform)
# output correlation matrix rr[][], density proportional to
# det(R)^{eta-1}
rcorcvine<-function(d,eta=1)
{
d<-as.integer(d)
if(d<=0 || !is.integer(d))
{ stop("The dimension 'd' should be a positive integer!\n") }
if(eta<=0)
{ stop("'eta' should be positive!\n") }
#handling of d=1 and d=2
if(d==1)
{ rr<-matrix(1,1,1); return(rr) }
if(d==2)
{ rho<-2*rbeta(1,eta,eta)-1
rr<-matrix(c(1,rho,rho,1),2,2); return(rr)
}
rr<-matrix(0,d,d)
# matrix of partial correlations as generated
prr<-matrix(0,d,d)
diag(rr)<-1
for(i in 2:d)
{ alp<-eta+(d-2)/2
rr[1,i]<-2*rbeta(1,alp,alp)-1
rr[i,1]<-rr[1,i]
prr[1,i]<-rr[1,i]
}
for(m in 2:(d-1))
{ alp<-eta+(d-1-m)/2
for(i in (m+1):d)
{ prr[m,i]<-2*rbeta(1,alp,alp)-1
# back calculate thru lower order partials
tem<-prr[m,i]
for(k in (m-1):1)
{ tem<-prr[k,m]*prr[k,i]+tem*sqrt((1-prr[k,m]^2)*(1-prr[k,i]^2)) }
rr[m,i]<-tem
rr[i,m]<-rr[m,i]
}
}
return(rr)
}
#set.seed(1234)
#nsim=5000
##nsim=200
#d=5
#eta=d/2
##eta=2.2
##eta=3.5
#out5=matrix(0,nsim,d^2)
#for(i in 1:nsim)
#{ out5[i,]=c(rcorcvine(d,eta)) }
##d=5, OK
#par(mfrow=c(3,3))
#qqbeta(out5[,2],eta)
#qqbeta(out5[,3],eta)
#qqbeta(out5[,4],eta)
#qqbeta(out5[,5],eta)
#qqbeta(out5[,8],eta)
#qqbeta(out5[,9],eta)
#qqbeta(out5[,10],eta)
#qqbeta(out5[,14],eta)
#qqbeta(out5[,15],eta)
#
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.