I am trying to figure out the best way to model the error using lmer for a randomized complete block design (RDBC) where we measured soil nitrogen weekly for 2 years.
There are: 4 Blocks (A, B, C, D) 6 Treatments (0, 50, 75, 100, 150, 200 kg of N/ha/yr) 4 replicates = 24 Plots (1....24) 50 sampling Dates ConcNO3 is the independent variable
Treatment is the fixed effect, and Plots are nested within Blocks (random). Date can also be considered a random effect.
I am trying to decide between the the following models:
fit1 <- lmer(ConcNO3 ~ Treatment + (1|Treatment:Date) + (1|Date) + (1|Block/Plot), data = df)
Which includes an interaction effect between Treatment and Date
fit2 <- lmer(ConcNO3 ~ Treatment + (1|Date) + (1|Block/Plot), data = df)
Simplified model that does not include the effect between Treatment and Date
Do you think that I am on the right track or do I also need to nest Treatment within Block?