[R-sig-ME] how to code a hopefully simple mixed model with spatially autocorrelated variables
th|erry@onke||nx @end|ng |rom |nbo@be
Thu May 5 15:12:12 CEST 2022
1. Time is a 1D correlation structure. Depth can be thought of as a 1D
correlation too. So you can use similar structures.
2. Model building is as much an art as a science. The full model should
make sense. Don't include variables for which you can't explain their
relevance. Avoid confounding variables. Sometimes you can reduce the
confounding by creating new variables based on the available variables. You
might want to contact a local statistician.
3. Start with Poisson. Switch to negative binomial in case of
overdispersion. Have a look at the packages glmmtmb and INLA. They provide
more distributions than nlme and allow correlated random effects (which you
want to model the depth).
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
Havenlaan 88 bus 73, 1000 Brussel
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 do 5 mei 2022 om 14:06 schreef Tim Richter-Heitmann | Universitaet
Bremen <trichter using uni-bremen.de>:
> Dear group,
> i have the following data:
> outcome: Abundance data (likely negative binomial or Poisson),
> potentially autocorrelated
> environmental predictors: 8 continuous variables, potentially
> autocorrelated, potentially collinear.
> A Spatial structure of the measurement: the depth of the measurement
> into a sediment ("Core Depth"). Continuous, in cm. It has only one
> dimension, like a gradient.
> Location: a random effect. A factor with 7 levels with about 17 - 25
> observations each. The location of the measurement on the seafloor.
> These Locations are so far apart, that i cant imagine that they are
> autocorrelated. Thus, id like to use it as factor.
> I would like to model something like:
> gls(Abundance ~ Predictors + 1|Location, correlation = corGaus(~Depth))
> Here are some questions:
> 1. Is this ok? Since depth is linear and progressive, can it be
> treated like a temporal structure? Or could Depth be also modelled as
> a fixed effect? It is clear from the data that abundance varies by
> 2. What would be the best way to select the best explaining variables?
> 3. How to get the negative binomial distribution of the outcome into
> the model?
> I am very curious about your advise.
> Best, Tim
> Dr. Tim Richter-Heitmann
> University of Bremen
> Microbial Ecophysiology Group (AG Friedrich)
> FB02 - Biologie/Chemie
> Leobener Straße (NW2 A2130)
> D-28359 Bremen
> Tel.: 0049(0)421 218-63062
> Fax: 0049(0)421 218-63069
> R-sig-mixed-models using r-project.org mailing list
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