Dear all,
We want to test if the invasiveStatus is predicted by the amount (quant) of
animals arriving to a country of a certain species (taxonid). We are using lmer
to perform the model.
The model is:
lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country),
family=binomial,data=td),
where invasiveStatus is a binary variable, quant and inDegree are integer
variables, and taxonid and country are factor variables.
The fixef output is
(Intercept) I(log(quant + 1)) I(log(inDegree + 1))
-15.6338288 0.3198074 2.1566502
and the ranef output is, sorted from higher to lower, andshowing only the first
10 lines,
$taxonid
T16 9.51
T258 8.36
T388 8.24
T961 7.98
T76 7.48
T470 7.46
T108 7.17
T84 7.15
T292 6.91
T189 6.65
...
$country
US 3.23
JP 2.45
ES 2.35
IT 2.14
BM 1.63
IL 1.41
SI 1.39
LB 1.06
FR 1.05
VE 0.996
...
Our problem is that the coefficients to the final estimate of invasiveStatus
are higher for the random variables than the fixed ones. We think this is a
result of the confound effect between quant, and country and taxonid. In other
words, the higher the number of animals of a given species(taxonid) arriving to
given country, the higher the probability of other species to arrive to the
same country.
Are we formulating the model correctly? Is there a way to avoid that the
contribution of the random variables is the most contributing part to the final
estimate?
Thanks,
Luis Reino
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