pcorOrder {pcalg} | R Documentation |
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 variable numbers to compute partial correlations of. |
k |
conditioning set for partial correlations (vector of integers). |
C |
correlation matrix (matrix) |
cut.at |
number slightly smaller than one; if c is
cut.at , values outside of [-c,c] are set to -c or
c respectively. |
The partial correlations are computed using a recusive formula.
To avoid numeric problems in recursions, the
partial correlation is set to cut.at
(= 0.9999999 by
default; keep this value, unless you know better!), if it is above
(e.g., if 1) and to -cut.at
if it is below (e.g., if -1).
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)