My data consist of 4 treatments (fixed), 8 locations (2 locations per treatment), 3 positions (per location, fixed), 3 samples (per location, random), with dependent variable
Nmin observed at 2 sample times.
I want to do a nested repeated-measures ANOVA in R. I believe this falls under the name 'mixed effects model' (?)
As nested and repeated-measures have different assumptions I was wondering how to handle this?
Also, should I first carry out a normal nested ANOVA and normal repeated-measures, and then the mixed model effects? Does this give more information or is it a waste of time?
I want to use the mean of the 3 samples but I don't want to loose information; might a range be the handiest? I am unsure of how to code this into the R formula (either mean or range).
These are my formulae (I have not run them yet as I am a bit confused about the assumptions and don't think the formulae are correct yet):
lme(data=data, fixed=Nmin~sample/position*time, random=~1|(sample/position/location)) ezANOVA(data=data, dv=Nmin, wid=sample, within=time, between=location) aov(Nmin~treatment*position*location*time + error(sample/position/location), data=data)
I want to see if (1) position, (2) location, (3) treatment, (4) time and (5) every combination of (1) - (4), have an affect on
p*tr*ti. I am unsure how to handle
p is nested in
l, but I am interested in the affect of location on
Could anyone explain to me how to handle the assumptions? How can I add 'means of sample' to my R formulae? How should I formulate my R formulae taking into account my numerous research questions?