Thank you very much for your help Bob. I think that's a great idea.
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From: R-sig-ecology [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of
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Sent: 07 April 2016 11:00
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Subject: R-sig-ecology Digest,
Dear Jean-Yves,
The mailing strips HTML and most of the attachment. Hence we can't see the
plots.
Note that you need the normalised residuals to see the effect of the
correlation structure. resid(type = "normalized").
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / R
Dear all,
I'm trying to fit lme models on spatial-temporal data with strong
inter-annual autocorrelation.
The response variable is a bird diversity measure sampled on several
plots during 10 to 40 years, which I want to relate to environmental
covariates. To give an idea of the sample size, th
I think one way of making correct inferences from similar problems would
be to make a Bayesian regression with error in variables and grouped
errors.
Using Laplaces demon in R (the package is not anymore in CRAN but seems
still to run and can be downloaded from the maintainer (an enterprise
called
On 06/04/16 20:12, Aislinn Pearson wrote:
Hi,
I've tried googling this but haven't been very successful. Essentially, I'd
like to know what is the most statistically valid way of dealing with a random
term which doesn't apply to every level of fixed-effect factor.
I have a mixed effect model
