Is there an R function for computing a variance-covariance matrix that
guarantees that it will have no negative eigenvalues? In my case, there
is a *lot* of missing data, especially for a subset of variables. I think
my tactic will be to compute cor(x, use="pairwise.complete.obs") and then
pre- and post-multiply by a diagonal matrix of standard deviations that
were computed based on all non-missing observations. Or maybe cov() would
do exactly that with use="pairwise.complete.obs", but that isn't really
clear from the docs. Next I would test to see if what I have is positive
definite. If the correlation matrix is positive definite, then the
covariance matrix will be.
Maybe I'll be lucky, but I need a positive-definite matrix, and this
method is not guaranteed to produce one. Any ideas?
Mike
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