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MARpdfclaytongumbeljoe.r
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MARpdfclaytongumbeljoe.r
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# MARpdfclaytongumbeljoe
#--------------------------- MARclaytonpdf
library(copula)
library(rgl)
M=10000 # number of Monte Carlo simulation
rho=.7 # single parameter in correlation matrix of the 3-dimensional Gaussian copula
d=2 # dimension of the Gaussian copula
theta2Input=0.7 # Kendall's tau
clayton.cop <- claytonCopula(iTau(claytonCopula(), theta2Input), dim = d)
U <- rCopula(M, clayton.cop )
u1=U[,1]
u2=U[,2]
ng=33 # number of grid
x=seq(0,1,length.out=ng) # grid elements
y=seq(0,1,length.out=ng)
### function for usage in outer function
fhat=function(x,y){
u=cbind(x,y)
v=numeric()
v=dCopula(u, clayton.cop, log=FALSE)
return(v)
}
### use outer function
outer931=outer(x,y,fhat)
### 3d PDF plot
open3d()
persp3d(x,y, outer931,
theta =35, phi = 35,
col="blue",
xlab = "u1", ylab = "u2", zlab = "c(u1, u2)",front="fill",back="line",alpha=0.9,
ticktype = "detailed",aspect=c(1,1,1.3))
box3d()# add a framework
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col="white")
contour(outer931, drawlabels=T, nlevels=33, col=2, add=TRUE,cex=10)
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col=1)
#--------------------------- MARgumbelpdf
library(copula)
library(rgl)
M=10000 # number of Monte Carlo simulation
rho=.7 # single parameter in correlation matrix of the 3-dimensional Gaussian copula
d=2 # dimension of the Gaussian copula
theta2Input=0.7 # Kendall's tau
gumbel.cop <- gumbelCopula(iTau(gumbelCopula(), theta2Input), dim = d)
U <- rCopula(M, gumbel.cop )
u1=U[,1]
u2=U[,2]
ng=33 # number of grid
x=seq(0,1,length.out=ng) # grid elements
y=seq(0,1,length.out=ng)
### function for usage in outer function
fhat=function(x,y){
u=cbind(x,y)
v=numeric()
v=dCopula(u, gumbel.cop, log=FALSE)
return(v)
}
### use outer function
outer931=outer(x,y,fhat)
### 3d PDF plot
open3d()
persp3d(x,y, outer931,
theta =35, phi = 35,
col="blue",
xlab = "u1", ylab = "u2", zlab = "c(u1, u2)",front="fill",back="line",alpha=0.9,
ticktype = "detailed",aspect=c(1,1,1.3))
box3d()# add a framework
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col="white")
contour(outer931, drawlabels=T, nlevels=33, col=2, add=TRUE,cex=10)
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col=1)
#--------------------------- MARjoepdf
library(copula)
library(rgl)
M=10000 # number of Monte Carlo simulation
rho=.7 # single parameter in correlation matrix of the 3-dimensional Gaussian copula
d=2 # dimension of the Gaussian copula
theta2Input=0.7 # Kendall's tau
joe.cop <- joeCopula(iTau(joeCopula(), theta2Input), dim = d)
U <- rCopula(M, clayton.cop )
u1=U[,1]
u2=U[,2]
ng=33 # number of grid
x=seq(0,1,length.out=ng) # grid elements
y=seq(0,1,length.out=ng)
### function for usage in outer function
fhat=function(x,y){
u=cbind(x,y)
v=numeric()
v=dCopula(u, joe.cop, log=FALSE)
return(v)
}
### use outer function
outer931=outer(x,y,fhat)
### 3d PDF plot
open3d()
persp3d(x,y, outer931,
theta =35, phi = 35,
col="blue",
xlab = "u1", ylab = "u2", zlab = "c(u1, u2)",front="fill",back="line",alpha=0.9,
ticktype = "detailed",aspect=c(1,1,1.3))
box3d()# add a framework
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col="white")
contour(outer931, drawlabels=T, nlevels=33, col=2, add=TRUE,cex=10)
### scatter plot and contour
plot(u1,u2, xlab="u1", ylab="u2", pch=19, cex=.1,col=1)