I have a data matrix X (n x k, say) each row of which constitutes an
observation of
a k-dimensional random variable which I am willing, if not happy, to
assume to be
Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct
rows of X may
be assumed to correspond to independent realizations of this random
variable.
Most rows of X (all but 240 out of 6000+ rows) contain one or more
missing values.
If I am willing to assume that missing entries are missing completely
at random (MCAR)
then I can estimate the covariance matrix Sigma via maximum
likelihood, by
employing the EM algorithm. Or so I believe.
Has this procedure been implemented in R in an accessible form? I've
had a bit of a
scrounge through the searching facilities, and have gone through the
FAQ, and have
found nothing that I could discern to be directly relevant.
Thanks for any pointers that anyone may be able to give.
cheers,
Rolf Turner
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