In R language, I want to solve the weight coefficients of a multidimensional hybrid copula, it is better to be able to use different copula functions, or for a fixed copula function(clayton,gumbel,frank copula) if the conditions are restricted. Use EM algorithm or some other algorithm to calculate the weight coefficients. Thank you very much for your professional advice and guidance!
library(copula)
### Input three-dimension data :
x1 <- rnorm(n=1000,mean = 0.5,sd = 0.1)
x2 <- rlnorm(n = 1000,meanlog = 0.5,sdlog = 0.01)
x3 <- runif(n = 1000,min = 0.2,max = 0.8)
data<-matrix(c(x1,x2,x3),1000,3)
### mixture copula type: clayton + gumbel + frank copula (or + normal copula \ t copula)
###for example:
copula1 <- mixCopula(list( claytonCopula (param = 1.3,dim = 3),
frankCopula (param = 2.718,dim = 3),
gumbelCopula (param = 4.58,dim = 3)))
###EM algothrim.
### output : weight of mixture copula
To form a mixture copula model, you can use the 'Copula' package in R.
Here you go:
MixCop <- mixCopula(list(frankCopula(9,dim=3), claytonCopula(4, dim=3))) ## form the model
CopD <- rCopula(300, MixCop) ## generate the data
Res <- fitCopula(CopD) ## estimate the model
library(copula)
### Input three-dimension data :
x1 <- rnorm(n=1000,mean = 0.5,sd = 0.1)
x2 <- rlnorm(n = 1000,meanlog = 0.5,sdlog = 0.01)
x3 <- runif(n = 1000,min = 0.2,max = 0.8)
data <- cbind(x1, x2,x3)
Copdat <- pobs(data)## transform the data to copula data
### mixture copula type: clayton + gumbel + frank copula (or + normal copula \ t copula)
###for example:
copula1 <- mixCopula(list( claytonCopula (param = 1.3,dim = 3),
frankCopula (param = 2.718,dim = 3),
gumbelCopula (param = 4.58,dim = 3)))
cop <- fitCopula(copula1, Copdat)
> cop <- fitCopula(copula1, Copdat)
Warning messages:
1: In .local(copula, tau, ...) :
For the Gumbel copula, tau must be >= 0. Replacing negative values by 0.
2: In fitCopula.ml(copula, u = data, method = method, start = start, :
var.mpl(copula, u) failed: system is computationally singular: reciprocal condition number = 6.5821e-29
> cop
Call: fitCopula(copula1, data = Copdat)
Fit based on "maximum pseudo-likelihood" and 1000 3-dimensional observations.
Copula: mixExplicitCopula
m1.alpha m2.alpha m3.alpha w1 w2 w3
3.893e+00 8.883e-01 1.000e+00 5.682e-14 1.060e-10 1.000e+00
The maximized loglikelihood is -2.649e-06
Optimization converged
Hope this helps