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I don't know if it's a way toward the answer, but I do not understand the logic behind the first model. If RatID is the random effect, and assuming each rat really has a different ID, I do not see what the sp_des:Rat additional random effect term really means. A syntax like (1|RatID) would have been expected, for random effects on intercepts only. Especially ...


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If you have a decent number of subjects, it's fine to show the mean of the individual proportions of correct responses (correct looks). In one sense, it's better than taking the proportion of correct responses while ignoring the clustering by patient. You essentially have individual estimates of the probability of a correct response, and you are averaging ...


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You appear to be asking about a study design which includes 2 levels of nesting, for example this would be the case in a study that made repeated measurements within many schools, and also repeated measurements within classrooms within the schools. So here you would have classrooms nested within schools. Each classroom "belongs" to a particular school. This ...


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Neither of your model formulations make sense. Both b and c should be treated as fixed effects. Since observations are repeated within subjects, you should use the subject ID as the grouping variable. So, the following model would be a good place to start: a ~ b + c + (1 | subjectID) which will fit random intercepts for each subject. Also Is there a ...


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The between-within group interaction can be interpreted as such: does the difference between moments (within) depend on the treatment group that they are in (between)? The math behind this is very simple but important. Let's say you only had moments 1 and 2. We would find the interaction by subtracting moment 2 from moment 1, and use that value as a ...


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If I understand your second option correctly, you'd wind up with 300 * 200 datapoints. This probably didn't work because of the fact that the errors for a given participant's measurements will almost surely be correlated. I think you have realized this already, since you mention that you want to "predict variation within participant". This sounds exactly ...


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When you include a random intercept in the model you say that there is variability in the value of the outcome at baseline between individual patients, you do not say something about the average of the outcome in different groups of patients. That is, it can well be that because of randomization the three arms have the same average at baseline, but still, ...


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After speaking with a statistician, he advised me to : Conduct two Friedman tests, one for the experimental and another for the control group. These two different tests are needed because a single Friedman test cannot be used on two independent groups. The test can be applied to only one independent group. However, it is possible to compare the results of ...


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