[R] Covariance of data with missing values.

[Chuck Cleland]

Ted Harding wrote:
> 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
> 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.

  Also, consider mlest() in the mvnmle package.

> 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
> 
> --------------------------------------------------------------------
> E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 15-Aug-07                                       Time: 23:17:48
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-- 
Chuck Cleland, Ph.D.
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