[R-sig-ME] help with repeated measures on a split-plotexperiment
Thierry.ONKELINX at inbo.be
Mon Jun 9 15:34:19 CEST 2008
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.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
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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
Thank you very much for the timely, helpful response. My apologies for
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
> 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
both Pinheiro & Bates and West, I was initially taking a more rigid
to developing my model following the examples in the texts.
B. Are you sure there is likely to be a measurable autoregressive
> 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
> (1995, 2000, 2007), or is this a sub-sample? With only three years,
> these not being sequential years, you may be asking too much of your
This is not a sub-sample - I only have 3 years of data. I assumed that
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
> fixed effect? With this included, are there any degrees of freedom
> the residual error, you'll need to get your replication from
> warned that lmer does seem to give results even when all dfs are used
> fixed effects and their interactions (I'm not sure why), but you need
> 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.
can run the model you have suggested as a lme function too but my dfs
ok for this analysis.
Using this model in an lme function, I have been unable to identify
III SS rather than Type I. Is there a straightforward way to obtain Type
Thanks again for the advice and comments - I appreciate them.
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