Dear users,

I am relatively new to the world of mixed models and was hoping someone
could offer some insight into something that is currently confusing me.
Probably the easiest thing to do is to describe a hypothetical experiment.

Say we have a field based experiment which aims to predict the presence of
an animal over time based on a treatment with two levels; supplimenting the
number of nesting sites vs natural number of nesting sites. In the
experimental area we create 2 blocks due to an environmental gradient (I
know this is not many but lets say  blocking opportunities are limited) and
within each block we have 14 plots. To each plot we randomly apply only one
of the treatments, giving a total of 7 supplimented and 7 natural plots per
block (n=14 per treatment level over both blocks). The presence of an animal
is recorded every day for two weeks for each plot.

Two potential models we could fit are

model1 <- lmer(pres ~ treatment + day + block + (1|plot), family = binomial)

model2 <- lmer(pres ~ treatment + day + block + (day|plot), family =
binomial)

where we use treatment, day  and block as fixed effects and plot or day|plot
as random effects.

Here is my point of confusion. As plot and treatment are potentially
confounded (i.e. each plot has only one level of treatment applied) can I
still use plot as a random effect? Would this approach just lead to a very
small between plot variance estimate due to the fixed effect of treatment?

Maybe a better approach would be to nest plot within treatment?

model3 <- lmer(pres ~ treatment + day + block + (1|treatment/plot), family =
binomial).

Many thanks for any insight offered
Martin

	[[alternative HTML version deleted]]


