metaSEM (version 1.3.1)

is.pd: Test Positive Definiteness of a List of Square Matrices

Description

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.

Usage

is.pd(x, check.aCov=FALSE, cor.analysis=TRUE, tol=1e-06)

Value

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.

Arguments

x

A square matrix or a list of square matrices

check.aCov

If it is TRUE, it mirrors the checking in asyCov.

cor.analysis

Whether the input matrix is a correlation or a covariance matrix. It is ignored when check.aCov=FALSE.

tol

Tolerance (relative to largest variance) for numerical lack of positive-definiteness in x. It is adopted from mvrnorm.

Author

Mike W.-L. Cheung <[e-mail]>

Examples

A <- diag(1,3)
is.pd(A)
# TRUE

B <- matrix(c(1,2,2,1), ncol=2)
is.pd(B)
# FALSE

is.pd(list(A, B))
# TRUE FALSE

C <- A
C[2,1] <- C[1,2] <- NA
is.pd(C)
# NA