[R] Array as time series?
P.S.Cowpertwait at massey.ac.nz
Fri Sep 7 00:03:37 CEST 2001
If your data is a bit like rainfall at multiple sites, I could probably
offer some comments (assuming (x,y,z,t) is location and time?), although I
don't know how useful they will be for your particular application.
My (limited) experience is that R will do quite a lot up to bivariate time
series (e.g. ccf function), but beyond that you need to write your own code.
This could be in R, but may be more appropriate in a fully compiled language
(like C or Fortran) if you have a large data set (because R is too slow for
heavy number crunching work; although it appears to be faster than S-Plus).
Again I'm not sure whether this is relevant for your data set, but a fairly
common approach with climatological data is to attempt to reduce the
dimensions using principle components. I understand PCA functions exist in
R, although I've never used them. Depending on your data this approach may
simplify the problem.
From: Christian Georges [mailto:christian.georges at uibk.ac.at]
Sent: Friday, 7 September 2001 02:48
To: r-help at stat.math.ethz.ch; Christian Georges
Subject: [R] Array as time series?
I have 4-dimensional atmospheric data (x,y,z,t), which I want to analyse
on spatio-temporal diversities.
As far as I understand there only exists the possibility to construct
time series as two-dimensional matrices (mts).
For the moment, I hold it in different objects:
1. a four-dimensional array for the spatial related analyses
2. a two-dimensional mts timeserie, which was derived from 1. by
computing spatial means.
But, still this doesn't help for combined spatio-temporal analysis.
One could regard the time dimension as just another linearily spaced
dimension in the four-dimensional array, but when it comes analyses and
graphics output it gets complicated, since one can't use all timeseries
(ts-) related functions (require ts attributes).
Could you provide me some comments, workarounds or your experiences on
Thanks in advance,
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