[R-sig-ME] nlmer model definition and starting values
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Wed Apr 29 15:25:21 CEST 2009
Dear Sebastian,
The names of the starting values should be identical to the names of the
coefficients in your model. A named vector has the advantage that the
order does not matter. With an unnamed vector the order should be
indentical as the order of the coefficients in your model.
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and 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-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Sebastian P.
Luque
Verzonden: woensdag 29 april 2009 15:07
Aan: r-sig-mixed-models at r-project.org
Onderwerp: Re: [R-sig-ME] nlmer model definition and starting values
On Wed, 29 Apr 2009 09:52:31 +0200,
"ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be> wrote:
> Dear Sebastian, There is no need to define SSGompertz as SSgompertz is
> already included in the stats package.
> The helpfile for nlme() gives you a good example. Switching SSamymp
> into SSgompertz is rather obvious.
> fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly,
> fixed = Asym + R0 + lrc ~ 1, random = Asym ~ 1, start = c(Asym = 103,
> R0 = -8.5, lrc = -3.3)) summary(fm1) fm2 <- update(fm1, random =
> pdDiag(Asym + lrc ~ 1)) summary(fm2)
Thanks Thierry. I was aware of SSgompertz(), but it is not the
parameterization that I need, and I don't know how I would have to
back-transform its results so that they correspond to the model I
showed. I was also not sure how to define the call to nlmer() with the
fixed and random effects I described, and particularly how to set up the
start values. The latter needs a named list or a vector, but I have
trouble anticipating what those names should be or the order of elements
in such a vector.
Cheers,
--
Seb
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