The old fashioned solution is to have the N x N x T
array and use character strings of the dates as the
dimnames on the third dimension.
Is there something you think you need to do that is
hard with such a setup?
Patrick Burns
patr...@burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")
Derek Schaeffer wrote:
Hi,
I am inquiring as to what are the best practices with respect to storing and
manipulating ordered multi-dimensional arrays. For example, suppose I have
a sequence of time-varying covariance matrices of asset returns. The data
is ordered, but the ordering is not necessarily regular (e.g. daily data
omitting weekends and holidays, etc.). The data array is say, N x N x T.
For example, the first two elements may look as follows:
*result$covariance[,,1:2]
, , 1*
* [,1] [,2] [,3] [,4]
[1,] 1.511137e-06 1.918668e-06 1.201553e-06 3.205271e-06
[2,] 1.918668e-06 7.488916e-06 6.593317e-06 1.203421e-05
[3,] 1.201553e-06 6.593317e-06 1.305861e-05 2.132272e-05
[4,] 3.205271e-06 1.203421e-05 2.132272e-05 4.571225e-05*
*, , 2*
* [,1] [,2] [,3] [,4]
[1,] 1.500858e-06 1.905574e-06 1.193412e-06 3.183290e-06
[2,] 1.905574e-06 7.444871e-06 6.555459e-06 1.195876e-05
[3,] 1.193412e-06 6.555459e-06 1.297075e-05 2.117777e-05
[4,] 3.183290e-06 1.195876e-05 2.117777e-05 4.551706e-05*
I would like to be able to partition this sequence of matrices by date and
by individual element. Partitioning by individual elements is trivial;
however, partitioning by time stamp is not (especially if the partitioned
data set must be carried through a number of downstream calculations). I
could carry the data in a list complete with a date vector and the data
array, and partition the list as I go, but this seems somewhat clunky. Any
ideas? A "zoo"-like package capable of handling multidimensional arrays
would be optimal, but I don't believe this exists.
Thanks,
Derek
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