Need help with nested random/mixed effect model specification I am a newbie in meta-analysis and I need your opinion on the design of my random-effect model.
I have conducted an experiment on the performance of a provider who has around 30-40 data centres. I picked two such data centres at random and monitored their performance for 7 consecutive weekdays. I took 2 measurements per day (1 during the peak and 1 during the off-peak). Now I have three factors in my experiment design: data centres, day and time having 2, 7 and 2 levels respectively. I want to know how much each of these factors contribute to the variance in performance. I have the following nested model in my mind:
m1 <- lme(Performance ~ 1, random =~ 1|DataCentre/Day/Time, data=mydata)

I am pretty sure that the time factor is nested within day; however, not quite sure whether the data centre should be a part of the nested structure. I assume that each data centre may have its own pattern of variability across different days, that's why included it in the nested structure.
Do you think this model is correct? Is there any other approach that you can suggest? 
Thanks.
 A: Do not use a mixed model. Levels of a random factor should be exchangeable, this is the case only for datacenter. Day is problematic since there could be start/end of week effects. It is not even clear if the weekend is included? Looking at time, peak and off-peak are obviously not exchangeable. 
You should also have more than two levels for a random factor. Two is not enough, so even datacenter should not be a random factor. Otherwise datacenter would be a very good candidate for being modelled as a random factor.
I hesitate to suggest any alternative analysis since when considering all three experimental factors and their interactions, you just have one sample per group. Is there any valid reason to pre-suppose that the performance does not depend on the exact combination of datacenter, peak/off-peak and day of week? 
A: If you tested only 2 centers and only once at peak and off-peak hour on each day, the sample size is too small for day of week analysis. 
I think you can use here linear regression with interactions of other variables: 
lm(performance ~ DC * Time, data=mydata) 

Edit: The orange price example in using-lsmeans vignettes is very similar to your data and you may find it helpful: http://cran.r-project.org/web/packages/lsmeans/vignettes/using-lsmeans.pdf 
