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2b
2011 Jan 31
2
computing var-covar matrix with much missing data
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