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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,

Dear all, I am looking at habituation of dogs trotting on a treadmill. Each record is made of 40 to 50 data for the same variable (for example Peak). I get each record at several minutes (1, 2 and 4) for each session. And I have 4 sessions of training (one session a week). The aim is to study the effect of the factor (Minute) on the Peak variable, and to study the changes of this effect

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one