Correlations to partial correlations and vice versa for R-vines with independence, Gaussian and t-copulas.
RVineCor2pcor(RVM, corMat)
RVinePcor2cor(RVM)RVineMatrix() defining only the R-vine structure for
Cor2pcor and providing as well the partial correlations for
Pcor2cor.
correlation matrix
RVineMatrix with transformed partial correlations (for
Cor2pcor)
correlation matrix (for Pcor2cor)
The behavior of RVinePcor2ccor differs from older versions (<=
1.4). The RVM object is now normalized such that the order of the returned
correlation matrix conforms with the correlation matrix of the data. If
RVM$names are non-default, the initial ordering of the variables
cannot be traced back and the matrix has to be interpreted as indicated by
the row- and column names.
## create RVineMatrix-object for Gaussian vine
Matrix <- matrix(c(1, 3, 4, 2,
0, 3, 4, 2,
0, 0, 4, 2,
0, 0, 0, 2), 4, 4)
family <- matrix(c(0, 1, 1, 1,
0, 0, 1, 1,
0, 0, 0, 1,
0, 0, 0, 0), 4, 4)
par <- matrix(c(0, 0.2, 0, 0.6,
0, 0, 0.2, 0.6,
0, 0, 0, 0.6,
0, 0, 0, 0), 4, 4)
RVM <- RVineMatrix(Matrix, family, par)
## calculate correlation matrix corresponding to the R-Vine model
newcor <- RVinePcor2cor(RVM)
## transform back to partial correlations
RVineCor2pcor(RVM, newcor)$par
#> [,1] [,2] [,3] [,4]
#> [1,] 0.0 0.0 0.0 0
#> [2,] 0.2 0.0 0.0 0
#> [3,] 0.0 0.2 0.0 0
#> [4,] 0.6 0.6 0.6 0
## check if they are equal
all.equal(RVM$par, RVineCor2pcor(RVM, newcor)$par)
#> [1] TRUE