[R] Re peated Measures (lme?)

[francogrex]

Hello, I have a general data analysis question. I recently visited a 
lab where they are testing a new treatment and they had done the 
experiment several times on different dates. They repeated the 
experiment 3-5 times per day. And then for practical reasons they 
repeated the whole procedure for 5 days.(they wanted a large sample 
size but practically they couldn't handle more than 5-10 experiments 
per day). However there might have been some extra variation between 
different days because the experimenter changed, although same 
procedure was being followed. 
Below are the data: 

Control data: 
Cday1=c(5,2,5,3,4); 
Cday2=c(2,1,3,1); 
Cday3=c(7,6,4,11,10); 
Cday4=c(5,13,8,4,10,6); 
Cday5=c(21,8, 5, 5,11); 

Treatment data: 
Tday1=c(17,11,25,21,16); 
Tday2=c(17,7,12); 
Tday3=c(16,18,4,20,18,25); 
Tday4=c(17,20,29,17,19); 
Tday5=c(14,31,28,34); 

Then they decided to do a paired t.test on the "mean" per day to 
measure whether 
they can detect a difference between the Control and the Treatment, 
something like: 
t.test(c(3.8,1.75,7.6,7.66,10),c(18,12,16.83,20.4,26.75),paired=T) 

But I thought there was something wrong in that procedure, something 
missing but I couldn't figure out what exactly, my feeling was that 
they were not capturing the variability in the measurements taken 
within one day. I thought maybe the solution could be in linear mixed 
effects models (lme) but could that be used to have some sort of a 
p-value (or other) to say there is a difference or not between the 
2 conditions. Or maybe other procedures? Any ideas? Thanks
 
-- 
View this message in context: http://www.nabble.com/Repeated-Measures-%28lme-%29-tp16012010p16012010.html
Sent from the R help mailing list archive at Nabble.com.