Compute Partial Correlations
This function computes partial correlations given a correlation matrix using a recursive algorithm.
pcorOrder(i,j, k, C, cut.at = 0.9999999)
i,j |
(integer) position of variable i and j, respectively, in correlation matrix. |
k |
(integer) positions of zero or more conditioning variables in the correlation matrix. |
C |
Correlation matrix (matrix) |
cut.at |
Number slightly smaller than one; if c is
|
The partial correlations are computed using a recusive formula
if the size of the conditioning set is one. For larger conditioning
sets, the pseudoinverse of parts of the correlation matrix is
computed (by pseudoinverse() from package
corpcor). The pseudoinverse instead of the inverse is used in
order to avoid numerical problems.
The partial correlation of i and j given the set k.
Markus Kalisch kalisch@stat.math.ethz.ch and Martin Maechler
condIndFisherZ for testing zero partial correlation.
## produce uncorrelated normal random variables mat <- matrix(rnorm(3*20),20,3) ## compute partial correlation of var1 and var2 given var3 pcorOrder(1,2, 3, cor(mat)) ## define graphical model, simulate data and compute ## partial correlation with bigger conditional set genDAG <- randomDAG(20, prob = 0.2) dat <- rmvDAG(1000, genDAG) C <- cor(dat) pcorOrder(2,5, k = c(3,7,8,14,19), C)
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