sdcor2cov
Converts a lower triangular matrix with standard deviations on the diagonal and partial correlations on lower triangle, to a covariance (or cholesky decomposed covariance)
sdpcor2cov(mat, coronly = FALSE, cholesky = FALSE)
mat |
input square matrix with std dev on diagonal and lower tri of partial correlations. |
coronly |
if TRUE, ignores everything except the lower triangle and outputs correlation. |
cholesky |
Logical. To return the cholesky decomposition instead of full covariance, set to TRUE. |
testmat <- diag(exp(rnorm(5,-3,2)),5) #generate arbitrary std deviations testmat[row(testmat) > col(testmat)] <- runif((5^2-5)/2, -1, 1) print(testmat) covmat <- sdpcor2cov(testmat) #convert to covariance cov2cor(covmat) #convert covariance to correlation
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