vfcp (version 1.4.0)

prosim: Monte Carlo method

Description

Probability of the inside of an object as defined by CDF and survival. For this, the Monte Carlo method is used.

Usage

prosim(C, fam, tht, dm, no)

Arguments

C

single numeric; CDF value. Survival value is 1 - CDF.

fam

Family name copula. These can be: "clayton", "gumbel", "frank", "joe", "amh", "fgm".

tht

Copula parameter. If fam = "fgm", it must be a vector of size \(dm*(dm-1)/2 + 1\).

dm

Copula dimension

no

Monte Carlo sample size

Value

Probability

Examples

# NOT RUN {
tht = 10.6
cx = c(0.05, 0.1, 0.15, 0.25)
pro = c(0.99999, 0.9999, 0.999, 0.99, 24, 16, 8, 4)
dm = 4
fam = "gumbel"
marg = rep(c("weibull", "betapr"), 2)
xo = rep(c(200, 2.75, 16.5, 6.60), 2)
#===========================
kop2 = kopula(fam, tht, dm)
fmc = c("", "", "clayton", "gumbel", "frank", "joe")
pro = c(0.999999, 0.99999, 0.9999, 16, 8, 4, 2)
tm3 = list()
tmk = list()
# di = dm*(dm - 1)/2
for (k in 1:length(cx)){
  tm3 = gentruk(tht, fm=fam, C=cx[k], pro)
  tmk[[k]] = tm3
}
np = 5
no = 100000
ncx = length(cx)
p = array(0, c(np*ncx, 2))
colnames(p) = c("C", "p")
k = 0
for (i in 1:length(cx)){
  for (j in 1:np){
    k = k + 1
    p[k, 1] = cx[i]
    p[k, 2] = prosim(C = cx[i], fam, tht, dm, no)
  }
}
plst = list()
print(paste(fam, "dim =", dm, "tht =", tht, "n =", no, "nrep.", np))
for (k in 1:ncx){
  plst[[k]] = summary(p[p[, 1] == cx[k], 2])
  print(paste("cx =", cx[k]))
  print(plst[[k]])
}

# }