[R] computing var-covar matrix with much missing data

[Mike Miller]

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