I have several animal pairs pair
. For each, I repeatedly measured daily proportions of time they spent in contact time.con
(30-60 measurements for each group, 1 measurement per date
). I want to compare how much time different pairs spent in contact using lmer
and controlling for repeated measures. The pairs are permanent, so essentially pair = individual. Here is a simplified example:
pair date time.con
[1,] "1" "01.06.17" "0.12"
[2,] "1" "02.06.17" "0"
[3,] "1" "03.06.17" "0.11"
[4,] "2" "04.06.17" "0.34"
[5,] "2" "05.06.17" "0.02"
[6,] "2" "06.06.17" "0.07"
[7,] "3" "01.06.17" "0.14"
[8,] "3" "02.06.17" "0.26"
[9,] "3" "03.06.17" "0.1"
So, the fixed effect would be pair
. The question is, how do I control for repeated measures? If I use pair
as both fixed and random effect, the model, obviously, fails to converge:
`lmer(time.con ~ pair + (1|pair))`
I guess that's where I'm meant to use date
somehow (as nested in pair
?), but I cannot get the syntax right:
`lmer(time.con ~ pair + (1+pair|date))` (doesn't work)
I'm probably missing something simple, as I'm new both to R and lmm. Would appreciate any advice!
Best Answer
This seems to be a longitudinal study, with measurements over time for each pair. As a first step, based on the
date
variable you could construct the follow-up time variable, which is the time from the first measurement. Think however carefully if the first measurement really is the time 0 for each pair for your experiment or perhaps another date.Then, you include random effects for the
pair
grouping variable, but not include it also as a fixed effect. You could start with a random intercepts model, e.g.,This model postulates that the correlations over time within a pair remain constant. You could extend the model by assume that the correlations decrease with the time span between measurements using a random intercepts and random slopes model, e.g.,
To evaluate if you need the random slopes, you could do a likelihood ratio test, i.e.,