Rui responded to your first question graciously with a very simple
default answer -- subtract the residuals from your observations.
That's about as "manual" as you can be without using pencil and paper.
If you can't understand the source code but want to so that you can
understand how the *residuals* are calculated, it's best to get some
local help.
On 22-May-13, at 8:41 AM, Neuman Co wrote:
So I mean: How can I calculate them manually?
2013/5/22 Neuman Co <neumanc...@gmail.com>:
Thanks, but this does not help me, because first of all, I do not
know
how to look at the source code (just entering fitted() or
getAnywhere(fitted()) does not help,
second, your solution x-m$residuals does not be a solution, because
then the question is, where do the residuals come from?
2013/5/22 Rui Barradas <ruipbarra...@sapo.pt>:
Hello,
Since R is open source, you can look at the source code of
package forecast
to know exactly how it is done. My guess would be
x - m$residuals
Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000
Hope this helps,
Rui Barradas
Em 22-05-2013 15:13, Neuman Co escreveu:
Hi,
3 down vote favorite
1
I am interested in forecasting a MA model.Therefore I have
created a
very simple data set (three variables). I then adapted a MA(1)
model
to it. The results are:
x<-c(2,5,3)
m<-arima(x,order=c(0,0,1))
Series: x
ARIMA(0,0,1) with non-zero mean
Coefficients:
ma1 intercept
-1.0000 3.5000
s.e. 0.8165 0.3163
sigma^2 estimated as 0.5: log likelihood=-3.91
AIC=13.82 AICc=-10.18 BIC=11.12
While the MA(1) model looks like this:
X_t=c+a_t+theta*a_{t-1}
and a_t is White Noise.
Now, I look at the fitted values:
library(forecast)
fitted(m)
Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000
I tried different ways, but I cant find out how the fitted values
(3.060660, 4.387627 and 3.000000) are calculated.
Any help would be very appreciated.
--
Neumann, Conrad
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--
Neumann, Conrad
--
Neumann, Conrad
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-
guide.html
and provide commented, minimal, self-contained, reproducible code.
Don McKenzie, Research Ecologist
Pacific Wildland Fire Sciences Lab
US Forest Service
phone: 206-732-7824
Affiliate Professor
School of Environmental and Forest Sciences
University of Washington
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
