hello dear list!
as I am currently helping someone with their statistical analysis of a
count survey, I stumbled upon a very basic question upon model optimization:
when fitting a model like:
model<-lmer(abundance~(x+y+z)^3,family=poisson,...)
in which x,y,z are continuous abiotic parameters such as po4
concentration, no2-concentration, which terms / interaction terms would
you recommend removing FIRST?
the ones of lowest significance (i.e. the ones with highest p-value) OR
the ones with the most complex interaction structure (even though
p-values may be low-ish)?
another question just popped in my mind:
let's say I've reduced my model to significant terms:
y ~ temperature + po4 + po4:temperature
and I know that correlation between po4 and temperature is high. would
you say that this is reason enough to remove the interaction term?
any opinion is a welcome opinion!
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