[R-sig-ME] help with repeated measures on a split-plotexperiment

[ONKELINX, Thierry]

James,

Have you considered lmer(response ~ snow*warm*year + (year|plot), cass))
with year as a factor? That would allow for correlation between the
years. But I'm wondering if year as a fixed effect still makes sense
with that kind of random effects.

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 James Hudson
Verzonden: maandag 9 juni 2008 4:29
Aan: Mike Dunbar
CC: R-sig-mixed-models at r-project.org
Onderwerp: Re: [R-sig-ME] help with repeated measures on a
split-plotexperiment

Dear Mike,

Thank you very much for the timely, helpful response. My apologies for
the
delay in responding - I have been in the field.


On 5/28/08, Mike Dunbar <mdu at ceh.ac.uk> wrote:
>
> Dear James
>
> Some quick initial comments.
>
> A. You are probably trying to make a much too complex model. I think
what
> you want is:
>
> lmer(response ~ snow*warm*year (1|plot), cass))
>
> As plot is the only random effect, all the others are fixed.


I appreciate the simplicity of the model you have suggested. After
reviewing
both Pinheiro & Bates and West, I was initially taking a more rigid
approach
to developing my model following the examples in the texts.

B. Are you sure there is likely to be a measurable autoregressive
structure
> to the time series data beyond that which is accounted for by the plot
> random effect. Just looking at the data listing, there are only three
years
> (1995, 2000, 2007), or is this a sub-sample? With only three years,
and
> these not being sequential years, you may be asking too much of your
data.


 This is not a sub-sample - I only have 3 years of data. I assumed that
if I
were to include a repeated measures factor in my model, that I'd need to
supply a covariance structure.


C. I might be missing something but are you really interested in year as
a
> fixed effect? With this included, are there any degrees of freedom
left for
> the residual error, you'll need to get your replication from
somewhere. Be
> warned that lmer does seem to give results even when all dfs are used
up by
> fixed effects and their interactions (I'm not sure why), but you need
to be
> able to judge that you have not fitted a sensible model.


 I am interested in change over time so I need "time" as a fixed factor.
I
can run the model you have suggested as a lme function too but my dfs
seem
ok for this analysis.


> cheers
>
> Mike


 Using this model in an lme function, I have been unable to identify
Type
III SS rather than Type I. Is there a straightforward way to obtain Type
III
SS?

 Thanks again for the advice and comments - I appreciate them.

James

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