On Fri, 8 Jan 2010, Erin Hestir wrote:
I am trying to conduct a time series analysis on historic hydrologic data, but I cannot coerce it into class ts because it does not have regular sampling intervals (some years have 20 samples, other have 8). Specifically I am trying to perform a CUSUM or or other step change detection, but the packages all seem to require data as ts.
As Gabor already pointed out: the zoo package can be used to store such data.
If you want to use the strucchange package for detecting changes then you can do two things:
- use a plain data.frame (without ts or some other time series class) which can be easily produced via as.data.frame(zoo_obj). Then the axis annotation in graphics won't be the time axis but the standard unit interval (= proportion of data). This is not so pretty but all statistical interpretations are still correct. - If zoo_obj is just a univariate series and you want to conduct a CUSUM test for a change in the mean you can do cus <- gefp(zoo_obj ~ 1) plot(cus) plot(cus, functional = meanL2BB) and so on. gefp() is the only function in strucchange that automatically supports "zoo". hth, Z
Is there a way to coerce my data into ts while maintaining all of my samples? Or alternatively, can someone recommend a package that does not require data as ts? Thanks! -- Erin Hestir Center for Spatial Technology and Remote Sensing University of California Davis [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.