[R-sig-ME] spatial auto-correlation or more complicated pseudo-replication?
th|erry@onke||nx @end|ng |rom |nbo@be
Wed Apr 22 13:57:40 CEST 2020
Do you have information on all the nests or only on a sample of the nests?
In case you have data on every nest, then I would look at a simple model
with only treatment and an iid nest effect. Then see if there is spatial
autocorrelation. Variation at small ranges would indicate an effect of the
treatment of the neighbouring nests.
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
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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
Op wo 22 apr. 2020 om 11:44 schreef Thomas Merkling <
thomasmerkling00 using gmail.com>:
> Hi all,
> I'm wondering how to best model data from an experimental design
> involving a spatial component. This is a study on seabirds nesting on
> artificial cliffs: each nest has been attributed an experimental
> treatment (supplemented or not), while making sure that there was a
> variable proportion of surrounding nests of the opposite treatment. Our
> main goal was to investigate if laying date of a focal pair was
> influenced by its treatment and/or by the proportion of surrounding
> nests of the opposite treatment (hereafter, "Prop"), which we calculated
> at 3 different spatial scale (local, panel and global, see
> https://drive.google.com/open?id=1OrJQCkNfBO6KOBHSlkOoQyAdTrqtIdY8 for a
> visual representation).
> Hence, the treatment information of a focal pair is used in the
> "Treatment" predictor variable, but also in the calculation of "Prop"
> for the surrounding pairs (the number of pairs affected depending on the
> spatial scale considered), thereby leading to some pseudo-replication.
> Since this is dependent on the distance (i.e. "Prop" of pairs closer to
> a focal one are more influenced than pairs further away), we thought
> that accounting for spatial auto-correlation for be sufficient. We used
> the spaMM package to do so, and our models look something like:
> Laying ~ Treatment * Prop + Year + (1|PairID) + Matern(Y2011|x + y) +
> Matern(Y2012|x + y)
> with two Matérn correlation random effects (one for each year of the
> study) being included (x and y being the spatial coordinates of the nests).
> My question is: Is this random effect structure taking into account the
> fact that "Prop" of a focal pair depends on the "Treatment" of the
> surrounding pairs or not ? If not, how can we account for that?
> Thanks in advance for your help!
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