Hi Michael,
Thank you so much for your email. That really helped, and with a frequency of
24, I finally have some figures I can work with!!
I have a follow up question . Do you have any resources you would recommend
that would explain the difference between stl() and decompose()? I have been
reading the help files, and googling, but I am not finding the right
resources.Â
There are some notable difference between the "random" and the "remainder" in
each and I am unsure which  is correct for my purposes.Â
At this time, I can use the "random" calculation from decompose() to generate
an acf of the data after the seasonal patterns and trends are stripped
( ac f ( n a.omit(x.ts.decomp$random) ) ,
which is ultimately what I need, however, that isn't solid justification for
choosing that calculation.Â
Any pointers appreciated.
Cheers,
Mary Ann
----- Original Message -----
From: "R. Michael Weylandt" <michael.weyla...@gmail.com>
To: "Mary Ann Middleton" <mab...@sfu.ca>
Cc: r-help@r-project.org
Sent: Thursday, August 9, 2012 2:51:16 PM
Subject: Re: [R] POSIXct to ts
On Thu, Aug 9, 2012 at 3:30 PM, Mary Ann Middleton <mab...@sfu.ca> wrote:
>
> Hi,
>
> I have a dataframe (try.1) with  date/time and temperature columns, and the
> date/time is in POSIXct fomat. Sample included below.
>
> I would like to to try decompose () or stl() to look at the trends and
> seasonality in my data, eventually so that  I can  look at autocorrelation.
> Â The series is 3 years of water temperature with clearly visible seasonal
> periods.
>
> Right now, if I try decompose, I get the following error, w hich I beleive is
> because I haven't correctly defined a time series.
> "Error in decompose(try.1) : time series has no or less than 2 periods"
>
> I am stuck trying to go from POSIXct to as.ts
> Any suggestions on how to tackle this would be greatly appreciated.
>
> Sincerely,
> Mary Ann
>
> A sample of the data looks like this:
> 'data.frame':  26925 obs. of  2 variables:
>  $ date    : POSIXct, format: "2008-07-11 21:00:00" "2008-07-11
> 22:00:00" ...
>  $ DL_1297699: num  15.3 15.1 14.9 14.6 14.1 ...  date DL_1297699 1 Â
> Â 2008-07-11 21:00:00 Â Â 15.318
> 2 Â Â Â 2008-07-11 22:00:00 Â Â 15.127
> 3 Â Â Â 2008-07-11 23:00:00 Â Â 14.888
> 4 Â Â Â 2008-07-12 00:00:00 Â Â 14.553
> 5 Â Â Â 2008-07-12 01:00:00 Â Â 14.146
> 6 Â Â Â 2008-07-12 02:00:00 Â Â 13.738
> 7 Â Â Â 2008-07-12 03:00:00 Â Â 13.401
> 8 Â Â Â 2008-07-12 04:00:00 Â Â 13.088
> 9 Â Â Â 2008-07-12 05:00:00 Â Â 12.823
> 10 Â Â Â 2008-07-12 06:00:00 Â Â 12.630 and the dput(head(x,50) gives
> this output: structure(list(date = structure(c(1215810000, 1215813600,
> 1215817200,
> 1215820800, 1215824400, 1215828000, 1215831600, 1215835200, 1215838800,
> 1215842400, 1215846000, 1215849600, 1215853200, 1215856800, 1215860400,
> 1215864000, 1215867600, 1215871200, 1215874800, 1215878400, 1215882000,
> 1215885600, 1215889200, 1215892800, 1215896400, 1215900000, 1215903600,
> 1215907200, 1215910800, 1215914400, 1215918000, 1215921600, 1215925200,
> 1215928800, 1215932400, 1215936000, 1215939600, 1215943200, 1215946800,
> 1215950400, 1215954000, 1215957600, 1215961200, 1215964800, 1215968400,
> 1215972000, 1215975600, 1215979200, 1215982800, 1215986400), class =
> c("POSIXt",
> "POSIXct"), tzone = "UTC"), DL_1297699 = c(15.318, 15.127, 14.888,
> 14.553, 14.146, 13.738, 13.401, 13.088, 12.823, 12.63, 12.461,
> 12.413, 12.461, 12.703, 13.04, 13.497, 14.026, 14.553, 15.031,
> 15.366, 15.7, 15.819, 15.819, 15.7, 15.605, 15.461, 15.247, 14.984,
> 14.673, 14.337, 14.002, 13.666, 13.377, 13.137, 12.944, 12.823,
> 12.847, 13.016, 13.329, 13.762, 14.242, 14.697, 15.175, 15.581,
> 15.891, 16.034, 16.034, 15.939, 15.772, 15.581)), .Names = c("date",
> "DL_1297699"), row.names = c(NA, 50L), class = "data.frame")
>
Thank you for the dput()-ed data!
The "time series" object that stl() and decompose() expect doesn't
have time stamps -- rather it has a "start" and "end" marker as well
as a frequency. [For more details, see ?tsp]
With your described data, I'd imagine you'd have start = 2008 and
frequency = 365*24 (if you have hourly data and an underlying yearly
periodicity) but to work with the data you gave, lets suppose 12 hours
is a cycle. Note you don't have to give end because that's figured out
automatically from frequency and start.
x.ts <- ts(x[,2], start = 1, frequency = 12)
then I can
stl(x, "per")
decompose(x)
as desired.
Hope that helps,
Michael
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