Dear Mikkel,
You need to do some reading on terminology.
In your model the fixed effects are channel 1, 2 and 3. samplenumber is
a random effect and the error term is an error term
The model you described has the notation below. You do not need to
create the grouped data structure.
lme(channel0 ~ pos + samplenumber + channel1 + channel2 + channel3,
random = ~ 1 | samplenumber,
correlation = corAR1(value = 0.5, form = ~ pos | samplenumber),
data = channel.matrix)
HTH,
Thierry
PS There is a dedicated mailing list for mixed models:
R-sig-mixed-models
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces at r-project.org
> [mailto:r-help-bounces at r-project.org] Namens Mikkel Meyer Andersen
> Verzonden: donderdag 2 september 2010 13:30
> Aan: r-help at r-project.org
> Onderwerp: [R] nlme formula from model specification
>
> Dear R-community,
>
> I'm analysing some noise using the nlme-package. I'm writing
> in order to get my usage of lme verified.
>
> In practise, a number of samples have been processed by a
> machine measuring the same signal at four different channels.
> I want to model the noise. I have taken the noise (the signal
> is from position 1 to 3500, and after that there is only noise).
>
> My data looks like this:
> channel.matrix:
> pos channel0 channel1 channel2 channel3 samplenumber
> 1 3501 8 3 12 1 1
> 2 3502 3 7 0 14 1
> 3 3503 9 1 13 3 1
> 4 3504 3 7 3 14 1
> 5 3505 6 5 4 5 1
> 6 3506 7 0 16 0 1
> ...
> 495 3995 5 2 9 9 1
> 496 3996 2 4 6 10 1
> 497 3997 3 2 7 7 1
> 498 3998 2 4 3 9 1
> 499 3999 3 1 6 11 1
> 500 4000 0 3 6 7 1
> 2301 3501 1 4 3 9 2
> 2302 3502 3 3 4 13 2
> 2303 3503 4 1 8 5 2
> 2304 3504 3 1 10 2 2
> 2305 3505 2 3 5 8 2
> 2306 3506 0 5 8 2 2
> ...
>
> The model is
> channel0 ~ alpha_i + eps_{i, j} + channel1 + channel2 +
> channel3 where i is sample number, j is position, and:
> alpha_i: fixed effect for each samplenumber
> eps_{i, j}: random effect, here with correlation
> structure as AR(1)
> channel1, ..., channel3: fixed effect for each channel not
> depending on
> samplenumber nor position
>
> (And then afterwards I would model channel1 ~ ... + channel2
> + channel3 etc.)
>
> I then use this function call:
> channel.matrix.grouped <- groupedData(channel0 ~ pos | samplenumber,
> data = channel.matrix)
>
> fit <- lme(channel0 ~ pos + samplenumber + channel1 +
> channel2 + channel3,
> random = ~ pos | samplenumber,
> correlation = corAR1(value = 0.5, form = ~ pos | samplenumber),
> data = channel.matrix.grouped)
>
> Is that the right way to express the model in (n)lme-notation?
>
> Cheers, Mikkel.
>
> ______________________________________________
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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.
>
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