[R-sig-ME] how to code a mixed model with spatially autocorrelated variables in glmmTMB - erratum
Tim Richter-Heitmann
tr|chter @end|ng |rom un|-bremen@de
Mon May 9 15:43:25 CEST 2022
In mod.1 and mod.2, "Core" should read "Location".
Am 09.05.2022 um 15:41 schrieb Tim Richter-Heitmann:
> Dear group,
>
> dear Thierry,
>
>
> 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), //
> //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./
>
> In nlme, I have coded the model like:
>
> mod.0 <- lme(outcome~ environmental predictors,
> random = ~ 1|Location,
> correlation = corLin(form = ~ Depth),
> method="ML",
> data=total)
>
> (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),
> data=total, family=nbinom1)
>
> and
>
> mod.2 <- glmmTMB(Abundance ~ ar1(as.factor(Depth) + 0 | Core),
> data=total, family=nbinom1)
>
> (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!
>
> Cheers, Tim
>
>
>
>
>
> 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
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>> <https://www.inbo.be>
>>
>>
>> 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
>> Depth.
>> 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
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
--
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
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