I've fit a Linear Mixed Effects model to some "accuracy" scores for a study with rats. The fixed effects are
Age, and the random effect is the individual rat (
Age is included because the trials span a number of months (and
Age is measured in days; not all rats were the same age at the start of the study, for a number of reasons).
The resulting model in R was:
library(lme) library(car) #for the logit() function, since scores are percentages lmer(logit(Score) ~ Age*TrialNumber + (1|Rat))
I get negative values for
Age (the study is designed such that we predicted scores to actually go down with more experience, which indeed turned out to be the case). However, the interaction term is also significant, but with a positive estimate.
Fixed effects: Estimate Std. Error t value (Intercept) 1.182e+00 1.689e-02 70.00 Age -2.788e-03 2.849e-04 -9.78 TrialNumber -1.872e-06 9.344e-08 -20.04 Age:TrialNumber 2.123e-08 1.741e-09 12.20
How do I interpret this interaction? Does it mean that for repeated trials on the same day,
Score went up, or does it mean that older rats did better on the same TrialNumbers?