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?

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.
