Copula for 3 variables I want to make Copula analysis for 3 variables to understand the dependency. I applied following code to obtain $c_{1,2}$ and $c_{1,3}$
library(VineCopula)
u <- pobs(as.matrix(cbind(var1,var2)))[,1]
v <- pobs(as.matrix(cbind(var1,var2)))[,2]
selectedCopula_1 <- BiCopSelect(u,v,familyset=NA)
selectedCopula_1

k <- pobs(as.matrix(cbind(var1,var3)))[,1]
l <- pobs(as.matrix(cbind(var1,var3)))[,2]
selectedCopula_2 <- BiCopSelect(u,v,familyset=NA)

The results showed that these variables have t and Frank copula families respectively. If I understand correctly I need to obtain the family of ${2,3|1}$. Then I need to use that information to understand f(var1,var2,var3).
To be honest I am very new to this field and did not know how to understand it. Do you have any recommendation?
 A: First of all, you need to understand what is copula and vine copula.
**Update: If you are working with real data you should do the following:

*

*new_data <- pobs(your_data).


*res <- RVineTreeStructureSelect(new_data) ## This will select for you the structure of your data and provide you will all results you need.


*contourplot(res) ## This will show you the dependency structure for every two variables, including the 2,3|1 (if it is the selected vine structure for your data).


*Copula only works on standard uniform margins. So, if you have real data
you need to transform it into copula data using the pobs function.


*Copula allows you to fit any type of marginal distribution.


*Copula is hard in high-dimensional cases as it imposes the same dependency among all the variables.
Vine Copula:

*

*Vine copula can be identified as an extended version of the copula. It is very flexible and works with  d >= 3, where d is the number of variables.

For your question, it is a good idea to start reading and practicing the vine copula model using the well-known package VineCopula. To generate the 3 variables, you need to set up a lower triangular matrix, see the VineCopula package, and specify the type of bivariate copula and their corresponding parameters.
Once you generate your data, plotting the data is a very good start to understanding it. Your code is not helpful. So, just try to follow VineCopula step by step to understand your case.
