Description Usage Arguments Value Author(s) Examples

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.

1 |

`x` |
A square matrix or a list of square matrices |

`check.aCov` |
If it is |

`cor.analysis` |
Whether the input matrix is a correlation or a
covariance matrix. It is ignored when |

`tol` |
Tolerance (relative to largest variance) for numerical lack
of positive-definiteness in |

If the input is a matrix, it returns `TRUE`

, `FALSE`

or `NA`

. If the input is a list of matrices, it returns
a list of `TRUE`

, `FALSE`

or `NA`

.

Mike W.-L. Cheung <[email protected]>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

```
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".
[1] TRUE
[1] FALSE
[1] TRUE FALSE
[1] NA
```

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