(Partial) Correlations for R-Vine Copula Models
Correlations to partial correlations and vice versa for R-vines with independence, Gaussian and t-copulas.
RVineCor2pcor(RVM, corMat) RVinePcor2cor(RVM)
RVM |
|
corMat |
correlation matrix |
RVM |
RVineMatrix with transformed partial correlations (for
|
cor |
correlation matrix (for |
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 ## check if they are equal all.equal(RVM$par, RVineCor2pcor(RVM, newcor)$par)
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