Description Usage Arguments Details Value Author(s) Examples
An adjacency matrix is converted to a weighted partial correlation matrix, with positive-definiteness as a condition.
1 |
ggm |
Adjacecny matrix, elements in -1, 0, 1. |
minpcor |
Numeric. Minimum allowed value for partial correlation. |
maxpcor |
Numeric. Maximum allowed value for partial correlation. |
maxiter |
Integer. Maximum number of attempts to get a positive definite matrix. |
verbose |
Logical. Specify whether the function inform you of each iteration. |
At each iteration, a candidate partial correlation matrix is created by drawing numbers from an uniform distribution from minpcor
to maxpcor
. If the matrix is positive definite and the correlation matrix implied is also positive definite, the matrix is returned in output. If positive-definiteness cannot be reaced after maxiter
iterations, a non positive-definite matrix is returned with a warning.
pcm
, a partial correlation matrix with the same conditional independencies specified in the adjacency matrix ggm
given as input.
Giulio Costantini, Lourens Waldorp
1 2 3 4 5 6 7 8 9 10 11 12 13 | ggm <- matrix(
c(0, 1, 1, 0,
1, 0, 1, 1,
1, 1, 0, 1,
0, 1, 1, 0), ncol = 4)
pcm <- ggm2pcm(ggm, minpcor = 0.2, maxpcor = 0.9, maxiter = 1000, verbose = FALSE)
# plot the two matrices
par(mfrow = c(1,2))
net1 <- qgraph(ggm)
title(main = "adjacency matrix")
qgraph(pcm, layout = net1$layout)
title(main = "partial correlation matrix")
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