[R-sig-ME] Is my model correct (1 random effect + spatially structured outcome) ?

Thierry Onkelinx thierry@onkelinx @ending from inbo@be
Mon Jul 23 09:02:43 CEST 2018

Dear Tim,

lmer() from lme4 cannot handle correlation functions. lme() form nlme
can. But there the correlation is only within the most detailed level
of the random effects. Observations from different levels (here
sampling dates) are assumed to be independent. However they will share
the same parameters for the correlation function.

Another option would be to fit the model without spatial correlation
structure and then make a variogram of the residuals. It might be
harder to get a stable variogram with only 60 locations. If the
variogram indicates spatial correlation, then you will have to model

Also provide sensible starting values for the correlation function.
The default value for the range is very small, often resulting in a
very small fitted range.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
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

2018-07-22 21:26 GMT+02:00  <trichter using uni-bremen.de>:
> Dear list,
> i have already posted once about this dataset, however now with a different
> approach.
> My dataset consists of six sampling dates (several months apart) with 60
> sampling stations each (within 100 square meters).
> Initially, i wondered if i can calculate Tukey contrasts by sampling dates
> if they are possibly both fixed and random.
> This time, my approach is fairly basic. I would like to model the influence
> of some environmental predictors (e.g. pH) on my outcome.
> I dont think my stations (specified with x,y coordinates) have random
> intercepts (as they are close to each other), but they likely feature
> spatial autocorrelation.
> This time, i treat time as random, and since the sampling dates are months
> apart, and the sampling grid was always different, i assume there is no
> temporal autocorrelation or effects
> of repeated measures.
> So, i would then fit a model like this:
> model1 <- lmer(Outcome ~ Var1+Var2+...+(1|sampling date),
> correlation=corXXXX(1,form=~x+y), data=data, REML=false)
> (alternatively also as interaction between the fixed effect).
> Assuming that i have normally distributed outcomes (which i dont), is this a
> proper approach?
> Alternatively, i could fit a model for each of the six sampling dates
> independently, and not use random effects at all.
> Thank you!
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