I am looking at habituation of dogs trotting on a treadmill. I record the ground reaction force and I analyze it with several discrete variables (maximum, minimum,...)
For each variable, I get between 40 and 50 data per sample.
I record data at time 1 min, 2 min, and 4 min a day, and I have 4 days of measurement (one day a week). That means I have 12 samples : Day1_Min1, Day1_Min2, Day1_Min4, Day2_Min1,...
Furthermore, I have 28 dogs to analyze.
My questions is : when can I consider the data stabilized?
The problem is I am studying habituation with several sessions. It seems logical that values from each first minute could be very dissimilar to others. It is not a fully linear training system. I have already done some ANOVAs with "Day" and "Min" factors. I found a significant effect.
It is quite logical because the dog is learning. The question is then when does it stop learning? or more precisely when is it trained enough to be analyzed?
I could do comparisons among all samples with Student test, but it is surely a simple approach.
I can presuppose the maximal allowed variability for each variable : in the region of 5%.
I am really new to both R and stats so if these questions are very simple and I am just missing something, suggestions about good texts or examples on R would be great. I am generating data with Scilab, I have a single matrix corresponding to each dog. But I can change it if needed.
Any help would be greatly appreciated
Thanks
Laurent Fanchon DVM, MS Ecole Nationale Veterinaire d'Alfort France
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