[R-sig-ME] Which model types accept the correlation-argument from nlme?

[Thierry Onkelinx]

Dear Tim,

I'd go for glmmTMB or INLA because those packages allow for a negative
binomial or Poisson distribution *and* spatially correlated random
effects. Have a look at Zuur et al (2017) Beginner's Guide to
Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA

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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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2018-04-05 17:45 GMT+02:00 Tim Richter-Heitmann <trichter at uni-bremen.de>:
> Dear list,
>
> I am struggling a bit with finding a suitable approach for the following
> problem.
>
> I have 60 samples on a grid, which were sampled six times (bimonthly).
> The 60 samples were not resampled each time, instead the sampling
> location was shifted a few decimeters each time. So, i have six
> individual spatial grids.
>
> The outcome variable are counts of microbial marker genes.
>
> My task is to classically find significant differences in abundance for
> bacterial clades between dates.
>
> My test of choice is glht() from the multcomp packages. This requires a
> model-object from which it calculates Tukey-comparisons of means per
> data. So, classically:
>
> mod <- glm.nb(Abundance ~ Date, data=data)
>
> glht(mod, mcp(Date = "Tukey"),  mcp(Date = "Tukey"), vcov=vcovHC).
>
> Now, since i have likely spatially autocorrelated outcome data, and
> possibly random effects of the time, i set up
>
> amod.null <- lme(fixed=Abundance ~ Date, data = data, random = ~1| Date,
> method="ML") # i think i asked a question about this in this list some
> years ago
>
> and use the update function, applying amod.null to c("corExp",
> "corGaus", "corLin", "corRatio", "corSpher"). I check for the best AIC,
> and subject this model to glht().
>
> So far, so good (i hope).
>
> (Sidequestion: Is the comparison of categorical means of autocorrelated
> measurements affected by the same problems as e.g. performing regression
> analysis between two continous variables, of which at least one is
> autocorrelated?)
>
>
> It seems that lme() is only for normally distributed data, but my
> outcome variable is seemingly best modelled assuming its negative
> binomially distributed (this was tested with GAMs).
>
> So, i am looking for a model type, which is a) acceptable for glht() or
> other multiple comparison tests, b) allows fixed and random effects, c)
> allows correction of spatial autocorrelated outcome variables, d)
> accepts assumptions of negative-binomial / Poisson
>
> count data.  I was made aware that glmmPQL does a lot of the things i
> want, but it doesnt give p-values afaik, and is not usable with glht().
>
> I am now down to chosing between:
>
> - a glm.nb with correction for heteroscedasticity.
>
> - a lme with random effects and corrections for spatial autocorrelations.
>
> What would an experienced modeller do (i am not)?
>
> Thank you!
>
> --
> 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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>
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