Hi:
Try
str(u.ts)
class(u.ts)
That should give you more information about the type of object being input
to stl.
I tried the following, which worked on my system:
u <- rnorm(100)
u.ts <- ts(u, start = c(2001, 1), frequency = 12)
u.stl <- stl(u.ts, 'per')
plot(u.stl)
> sessionInfo()
R version 2
Hi everyone.
I'm having some troubles with STL function to decompose some data.
My issue is that I have monthly data from September 2005 up to August 2010
i. e. 60 observations.
I define it in the following way:
*u<-read.csv("C:/CELEBREX.csv",header = TRUE)
u.ts<-ts(u, start=c(2005,9), frequenc
#from the ?stl examples
g<-stl(nottem, "per")
g
#in g are the residuals, sesonal trend, and the remainder. It looks
like you are going to have to #model this decompostion to get at what
you want.
Stephen
On Tue, Sep 2, 2008 at 12:50 AM, Ryan <[EMAIL PROTECTED]> wrote:
> charter.net> writes:
>
charter.net> writes:
>
> I just realized after some tips and a little digging that what I was trying to
do "manually" has already been
> done. I was trying to fit my data using 'lm' then taking the "residual" data
and trying to do a spectral
> estimate (for seasonality) usiing fft and then pass
if your two points are valid and you would be willing to run a least
spuares regression on then you should be fairly comfortable with
using na.approx in the zoo package. If interpolating values is going
to give you an erroneous result then maybe this is not the correct
path. But have a look.
On
I just realized after some tips and a little digging that what I was trying to
do "manually" has already been done. I was trying to fit my data using 'lm'
then taking the "residual" data and trying to do a spectral estimate (for
seasonality) usiing fft and then passing the "residual" of all of t
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