[R] Covariance of data with missing values.

[(Ted Harding)]

Hi Rolf!

Have a look at the 'norm' package.

This does just what you;re asking for (assuming multivariate
normal, and allowing CAR missingness -- i.e. probability of
>>>>>> OOPS, I meant MAR!
missing may depend on observed values, but must not depend on
unobserved).

Read the documentation for the various function *very* carefully!
Drop me a line if you want more info.

Best wishes,
Ted.

On 15-Aug-07 21:16:32, Rolf Turner wrote:
> 
> 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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Date: 15-Aug-07                                       Time: 23:17:48
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