[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 350183 1211
2 3502370 141
3 350391 1331
4 3504373 141
5 350565451
6 350670 1601
...
495 399552991
496 3996246 101
497 399732771
498 399824391
499 3999316 111
500 400003671
2301 350114392
2302 3502334 132
2303 350341852
2304 350431 1022
2305 350523582
2306 350605822
...
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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Re: [R] nlme formula from model specification
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.onkel...@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-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] Namens Mikkel Meyer Andersen
Verzonden: donderdag 2 september 2010 13:30
Aan: r-help@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 350183 1211
2 3502370 141
3 350391 1331
4 3504373 141
5 350565451
6 350670 1601
...
495 399552991
496 3996246 101
497 399732771
498 399824391
499 3999316 111
500 400003671
2301 350114392
2302 3502334 132
2303 350341852
2304 350431 1022
2305 350523582
2306 350605822
...
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.
__
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.
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Please do not print this message unnecessarily.
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__
R-help@r
Re: [R] nlme formula from model specification
Dear Thierry,
Thanks for the quick answer. I'm moving this to r-sig-mixed-models
(but also posting on r-help to notify).
I reserved Mixed-effects models in S and S-PLUS by Pinheiro and
Bates, New York : Springer, 2000. Do you know any other good
references?
Cheers, Mikkel.
2010/9/2 ONKELINX, Thierry thierry.onkel...@inbo.be:
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.onkel...@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-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] Namens Mikkel Meyer Andersen
Verzonden: donderdag 2 september 2010 13:30
Aan: r-help@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.
__
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.
Druk dit bericht a.u.b. niet onnodig af.
Please do not print this message unnecessarily.
Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer
en binden het INBO onder geen enkel beding, zolang
