Yes, Achim is spot on.
> ge
[,1]
2010-01-04 09:30:00 1
2010-01-04 09:31:00 2
2010-01-04 09:34:00 3
2010-01-04 09:35:00 4
> msft
[,1]
2010-01-04 09:30:00 1
2010-01-04 09:32:00 2
2010-01-04 09:33:00 3
> merge(ge,msft,xts(,timeBasedSeq("20100104 09:30/20100104 09:35")),
fill=na.locf)
ge msft
2010-01-04 09:30:00 1 1
2010-01-04 09:31:00 2 1
2010-01-04 09:32:00 2 2
2010-01-04 09:33:00 2 3
2010-01-04 09:34:00 3 3
2010-01-04 09:35:00 4 3
Other tools that could be used to aggregate to coarser times (if you had
subseconds say) are to.period and align.time:
> (x <- xts(1:5, Sys.time() + sample(seq(1, 300, 10), 5)))
[,1]
2010-06-21 20:25:32 4
2010-06-21 20:26:52 3
2010-06-21 20:27:52 5
2010-06-21 20:29:12 2
2010-06-21 20:30:22 1
> align.time(x, 60)
[,1]
2010-06-21 20:26:00 4
2010-06-21 20:27:00 3
2010-06-21 20:28:00 5
2010-06-21 20:30:00 2
2010-06-21 20:31:00 1
> align.time(x, 60*2)
[,1]
2010-06-21 20:26:00 4
2010-06-21 20:28:00 3
2010-06-21 20:28:00 5
2010-06-21 20:30:00 2
2010-06-21 20:32:00 1
HTH
Jeff
On Mon, Jun 21, 2010 at 8:17 PM, Dirk Eddelbuettel <[email protected]> wrote:
>
> On 22 June 2010 at 02:50, Achim Zeileis wrote:
> | On Mon, 21 Jun 2010, Eric Zivot wrote:
> |
> | > I am trying to align an xts object containing irregularly spaced
> intra-day
> | > price data to a regularly spaced time clock so that I can do realized
> | > variance/covariance calculations. For example, I have two xts objects
> | > msftTrades and geTrades created by the RTAQ package (the time index
> variable
> | > is a timeDate object) :
> |
> | I suspect that xts has some high-level tools that could be leveraged for
> | this.
>
> Yup, I reckon that help(to.period) would be what Eric is looking for.
>
> --
> Regards, Dirk
>
> _______________________________________________
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--
Jeffrey Ryan
[email protected]
ia: insight algorithmics
www.insightalgo.com
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