pcalg (version 2.0-3)

pcorOrder: Compute Partial Correlations

Description

This function computes partial correlations given a correlation matrix using a recursive algorithm.

Usage

pcorOrder(i,j, k, C, cut.at = 0.9999999)

Arguments

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.

Value

• The partial correlation of i and j given the set k.

Details

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.

See Also

condIndFisherZ for testing zero partial correlation.

Examples

## 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)