[R-sig-ME] how to code a mixed model with spatially autocorrelated variables in glmmTMB
tr|chter @end|ng |rom un|-bremen@de
Mon May 9 15:41:37 CEST 2022
thank you very much for your valuable input.
Before i am going to ask my question, here is a reminder of my dataset:
i have the following data:
/outcome: Abundance data (likely negative binomial or Poisson), //
//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./
In nlme, I have coded the model like:
mod.0 <- lme(outcome~ environmental predictors,
random = ~ 1|Location,
correlation = corLin(form = ~ Depth),
(Depth as another predictor variable never improved the models, so i
stick to just formulate them as a correlation structure; corLin yielded
better AICs than CorAR1; i also checked collinearity and there is none).
However, the residual plots cleary show some heteroscedasticity towards
higher values, so i would like to switch to another family to model the
abundance (as they are counts).
In glmmTMB, i could code
mod.1 <- glmmTMB(Abundance ~ Some continous predictors+ (1|Core),
mod.2 <- glmmTMB(Abundance ~ ar1(as.factor(Depth) + 0 | Core),
(Note: mod.1 did not converge)
Here are my questions:
1. How can i combine both right hand sites in glmmTMB to have a model
that encompasses all parameters, just like mod.0 in mlme?
(it is that just concatening them yields either an error or ignores one
half of the model)
2. glmmTMB requires coordinates for spatial structures. I can code Depth
with an "all-zero"-Coordinate to have binary coordinates. Is this better
than using ar1?
Thanks for your advice and input!
Am 05.05.2022 um 15:12 schrieb Thierry Onkelinx:
> Dear Tim,
> 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).
> Best regards,
> ir. Thierry Onkelinx
> Statisticus / Statistician
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be <http://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
> 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 =
> 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
Dr. Tim Richter-Heitmann
University of Bremen
Microbial Ecophysiology Group (AG Friedrich)
FB02 - Biologie/Chemie
Leobener Straße (NW2 A2130)
Tel.: 0049(0)421 218-63062
Fax: 0049(0)421 218-63069
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