Kind regards,
Ivan _______________________ Ivan Alves mailto://[EMAIL PROTECTED] On 25 Mar 2004, at 23:36, Liaw, Andy wrote:
From: Ivan Alves
Thank you very much Brian. Indeed, by looking at ?tapply() this would do the job for regular time series (whose data-time INDEX renders itself naturally for " bundling data" in regular groups). However, with irregular time series the story is different, as some careful "bundling" is necessary prior to applying tapply(). Whereas this may not be a problem for months (creating strings month-year), it would be for weeks, for instance. Also the object returned would need to be converted again to a time series object with additional function calls. Furthermore, the function AggregateSeries() provides additional functionality, such as creating moving averages, rolling variances, minima and maxima, all with options to the same function call. I take from your response that there is no easy way out (an already existing function), and that some programming will be required.
So can we count on you to contribute this (and the cointegration that you
requested in another post)? You did read the message at the R startup
screen, didn't you?
Best, Andy
Kind regards,
Ivan _______________________ Ivan Alves mailto://[EMAIL PROTECTED] On 25 Mar 2004, at 22:51, Prof Brian Ripley wrote:
R itself has no support for irregular time series, but itdoes have anlooked for aaggregate method for regular ones. You need to look into whichever package is handling irregular time series. However, it seems to me that this is not a time series problem at all: you have a set of observations whose indices are data-times, and tapply() will do the job.
On Thu, 25 Mar 2004, Ivan Alves wrote:
S-Plus has the function AggregateSeries() whose name is self explanatory. For instance one can derive monthly series from daily ones by specifying end-of-period, averages, sums, etc. Isimilar function in the packages "its" and "tseries", but found nothing. I also help.searched() for aggregate to no avail. Would anybody be so kind to point me in the right direction?
-- Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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