cor.smooth {psych}R Documentation  Smooth a non-positive
definite correlation matrix to make it positive definite
Factor analysis requires positive definite correlation matrices.
Unfortunately, with pairwise deletion of missing data or if
using tetrachoric or polychoric correlations, not all
correlation matrices are positive definite. cor.smooth does a
eigenvector (principal components) smoothing. Negative eigen
values are replaced with 100 * .Machine$double.eps, the matrix
is reproduced and forced to a correlation matrix using cov2cor.
A correlation matrix or a raw data matrix.   Report all
abs(residuals) > cut   The smoothing is done by eigen value
decomposition. eigen values < .Machine$double.eps are changed to
100 * .Machine$double.eps. The positive eigen values are
rescaled to sum to the number of items. The matrix is recomputed
(eigen.vectors %*% diag(eigen.values) %*% t(eigen.vectors) and
forced to a correlation matrix using cov2cor. (See Bock, Gibbons
and Muraki, 1988 and Wothke, 1993).  This d