Message-ID: <20180722212633.Horde.LNEC8aEodR-H-slUFtECQon@webmail.uni-bremen.de>
Date: 2018-07-22T19:26:33Z
From: trichter m@ili@g off u@i-breme@@de
Subject: Is my model correct (1 random effect + spatially structured outcome) ?
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!