Dear all,
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