It tests the positive definiteness of a square matrix or a
list of square matrices. It returns
TRUE if the matrix is
positive definite. It returns
FALSE if the matrix is either
non-positive definite or not symmetric. Variables with
NA in the diagonals will be removed
before testing. It returns
NA when there are missing correlations even after deleting
the missing variables.
A square matrix or a list of square matrices
If it is
Whether the input matrix is a correlation or a
covariance matrix. It is ignored when
Tolerance (relative to largest variance) for numerical lack
of positive-definiteness in
If the input is a matrix, it returns
NA. If the input is a list of matrices, it returns
a list of
Mike W.-L. Cheung <[email protected]>
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Loading required package: OpenMx To take full advantage of multiple cores, use: mxOption(NULL, 'Number of Threads', parallel::detectCores()) "SLSQP" is set as the default optimizer in OpenMx. mxOption(NULL, "Gradient algorithm") is set at "central". mxOption(NULL, "Optimality tolerance") is set at "6.3e-14". mxOption(NULL, "Gradient iterations") is set at "2".  TRUE  FALSE  TRUE FALSE  NA
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