Description Usage Arguments Details Value References Examples
Bivariate Archimedean copula based on integrated positive stable Laplace transform
1 2 3 4 5 6 7 8 | pipsA(u,v,cpar)
dipsA(u,v,cpar)
pcondipsA(v,u,cpar) # C_{2|1}(v|u;cpar)
qcondipsA(p,u,cpar) # C_{2|1}^{-1}(p|u;cpar)
ripsA(n,cpar)
logdipsA(u,v,cpar)
ipsA.cpar2tau(cpar)
ipsA.tau2cpar(tau,mxiter=20,eps=1e-06, cparstart=0, iprint=F)
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u |
value in interval 0,1; could be a vector |
v |
value in interval 0,1; could be a vector |
p |
quantile in interval 0,1; could be a vector |
cpar |
parameter: could be scalar or vector (positive-valued) |
n |
sample size for ripsA, positive integer |
tau |
tau in interval (-1,1), could be a vector |
mxiter |
maximum number of Newton-Raphson iterations |
eps |
tolerance for convergence of Newton-Raphson iterations |
cparstart |
starting point for Newton-Raphson iterations |
iprint |
print flag for Newton-Raphson iterations |
For the reflected copula, the functions are pipsAr, dipsAr, pcondipsAr, qcondipsAr, logdipsAr.
cdf, pdf, conditional cdf, conditional quantile value(s) for pipsA, dipsA, pcondipsA, qcondipsA respectively;
log density for logdipsA;
random pairs for ripsA;
Kendall's tau for psA.cpar2tau;
copula parameter for ipsA.tau2cpar or parameter value for a given Kendall's tau.
Joe H and Ma C (2000). Multivariate survival functions with a min-stable property. Journal of Multivariate Analysis, 75, 13-35.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | u=seq(.1,.6,.1)
v=seq(.4,.9,.1)
cpar=.6
pp=pcondipsA(v,u,cpar)
vv=qcondipsA(pp,u,cpar)
print(pp)
print(vv)
tau=ipsA.cpar2tau(cpar)
print(tau)
tauv=seq(-.9,.9,.1)
cpar=ipsA.tau2cpar(tauv)
print(cpar)
set.seed(123)
udata=ripsA(500,cpar=cpar[15]) # tau=0.5
print(taucor(udata[,1],udata[,2]))
print(cor(udata,method="kendall"))
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