Joshua, Thanks. I am going to check.
On Mon, Mar 17, 2014 at 11:10 AM, Joshua Ulrich <josh.m.ulr...@gmail.com>wrote: > On Sun, Mar 16, 2014 at 6:27 AM, Bill <william...@gmail.com> wrote: > > Hello Pascal, > > Yes that is what I was worried about. The date-stamps are there and I > would > > like to use that information but I think using as.ts will not do this. > > Does anyone know how this is done? > > It cannot be done. The ts class is used to "represent data which has > been sampled at equispaced points in time." You need to look at the > changepoint package code (or ask the author(s)) to determine whether > or not the frequency attribute is used. > > > Thank you. > > > > > > On Wed, Mar 12, 2014 at 1:02 AM, Pascal Oettli <kri...@ymail.com> wrote: > >> > >> Hello, > >> > >> On Tue, Mar 11, 2014 at 8:45 PM, Joshua Ulrich <josh.m.ulr...@gmail.com > > > >> wrote: > >> > On Tue, Mar 11, 2014 at 12:14 AM, Bill <william...@gmail.com> wrote: > >> >> > >> >> Hello. I have a dataframe that has a date column. The intervals > between > >> >> dates vary. I want to convert this to a ts object. I was able to > >> >> convert it > >> >> to an xts object but the package I want to analyse this data with > >> >> (called > >> >> 'changepoint') does not seem to want to deal with xts. In the example > >> >> they > >> >> give they use the following: > >> >> > >> >> data(discoveries) > >> >> dis.pelt=cpt.meanvar(discoveries,test.stat='Poisson',method='PELT') > >> >> plot(dis.pelt,cpt.width=3) > >> >> cpts.ts(dis.pelt) > >> >> > >> >> and if I check: > >> >> str(discoveries) > >> >> Time-Series [1:100] from 1860 to 1959: 5 3 0 2 0 3 2 3 6 1 ... > >> >> > >> >> If I try with my data > >> >> str(testTSRad) > >> >> An 'xts' object on 2011-07-16 07:08:02/2013-09-20 01:25:48 > containing: > >> >> Data: num [1:501, 1] 76 77 79 86 79 79 85 86 89 88 ... > >> >> Indexed by objects of class: [POSIXct,POSIXt] TZ: > >> >> xts Attributes: > >> >> NULL > >> >> > >> >> where I used this: > >> >> > >> >> testTSRad=xts(radSampPerRegion[[2]][ > >> >> ,2],order.by=as.POSIXct(radSampPerRegion[[2]][ > >> >> ,1])) > >> >> > >> >> I get this: > >> >> > >> >> testt=cpt.mean(testTSRad) > >> >> Error in single.mean.norm(data, penalty, pen.value, class, > >> >> param.estimates) > >> >> : > >> >> Data must have atleast 2 observations to fit a changepoint model. > >> >> > >> > This is because of what ?cpt.mean says about the "data" argument: > >> > data: A vector, ts object or matrix containing the data within > >> > which you wish to find a changepoint. If data is a matrix, > >> > each row is considered a separate dataset. > >> > > >> > An xts object is a matrix (with an index attribute), so each row is > >> > considered a separate data set. Your object only has one column, > >> > hence only one observation per data set. Things will work if you drop > >> > the dimensions of your single-column xts object: > >> > testt <- cpt.mean(drop(testTSRad)) > >> > > >> >> My data is below. Is there a way to convert it to ts? > >> >> > >> > Yes, as is generally the case, use the "as" method: > >> > as.ts(testTSRad) > >> > > >> > >> But in this case, the time serie will have a frequency of 1, which is > >> inconsistent with irregular sampling. This probably will lead to > >> inaccurate results > >> > >> > Best, > >> > -- > >> > Joshua Ulrich | about.me/joshuaulrich > >> > FOSS Trading | www.fosstrading.com > >> > > >> > ______________________________________________ > >> > 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. > >> > >> Regards, > >> Pascal > >> > >> -- > >> Pascal Oettli > >> Project Scientist > >> JAMSTEC > >> Yokohama, Japan > > > > > [[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.
