Background information
Using different machine learning models, I predicted three different behavioural categories in cats (active, inactive, maintenance). I then created a dataframe that contained the counts and proportion of each behavioural category per day (6 days per cat; 12 cats total).
Data
I have a dataframe (df) that contains both the counts (Active, Inactive, Maintenance) and proportions (propAtive, propInactive, propMaintenance) of the different behavioural categories. See head and structure of the df below:
Model Cat_id Day Active Inactive Maintenance Total propActive propInactive propMaintenance Model2
1: C.RF1 Cho 1 days 1936 75672 8792 86400 0.02240741 0.8758333 0.10175926 C.RF1
2: C.RF1 Cho 2 days 1307 78236 6857 86400 0.01512731 0.9055093 0.07936343 C.RF1
3: C.RF1 Cho 3 days 1360 73784 11256 86400 0.01574074 0.8539815 0.13027778 C.RF1
4: C.RF1 Cho 4 days 2828 70666 12906 86400 0.03273148 0.8178935 0.14937500 C.RF1
5: C.RF1 Cho 5 days 2988 74130 9282 86400 0.03458333 0.8579861 0.10743056 C.RF1
6: C.RF1 Cho 6 days 1809 74477 10114 86400 0.02093750 0.8620023 0.11706019 C.RF1
Classes ‘data.table’ and 'data.frame': 1152 obs. of 11 variables:
$ Model : Factor w/ 16 levels "C.RF1","C.RF2",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Cat_id : Factor w/ 12 levels "Cho","George",..: 1 1 1 1 1 1 2 2 2 2 ...
$ Day : 'difftime' num 1 2 3 4 ...
..- attr(*, "units")= chr "days"
$ Active : int 1936 1307 1360 2828 2988 1809 2616 2697 2540 3796 ...
$ Inactive : int 75672 78236 73784 70666 74130 74477 74862 75588 74547 73742 ...
$ Maintenance : int 8792 6857 11256 12906 9282 10114 8922 8115 9313 8862 ...
$ Total : int 86400 86400 86400 86400 86400 86400 86400 86400 86400 86400 ...
$ propActive : num 0.0224 0.0151 0.0157 0.0327 0.0346 ...
$ propInactive : num 0.876 0.906 0.854 0.818 0.858 ...
$ propMaintenance: num 0.1018 0.0794 0.1303 0.1494 0.1074 ...
$ Model2 : Factor w/ 16 levels "H.RF1","C.RF1",..: 2 2 2 2 2 2 2 2 2 2 ...
- attr(*, ".internal.selfref")=<externalptr>
- attr(*, "sorted")= chr [1:3] "Model" "Cat_id" "Day"
What do I want to test?
Looking at the means for each behavioural categorie, some models seem to predict quite similarly, while some predict quite differently. Now I would like to test for significant differences in counts/proportions between the models, within each behaviour (e.g., does model C.RF1 predict active with a different proportion than C.RF2). In addition to this, I would like to see if 'Day' has an effect on the counts/proportions. So far, I have used a Dirichlet Regression to test for differences in the proportions between the models. As far as I could find, however, the Dirichlet is not quite suited for repeated measures. In addition to this, I have had some issues with the Dirichlet Regression, for which I have not found a solution yet. If you want to know the specifics of this, have a look here.
Question
I have tried to search for an alternative statistical test I can do to get the results I want and have found a few different possible alternatives (e.g., 1, 2, 3). However, I am not a statistician and do not know what test would best fit my data and question, and would really appreciate any advice regarding which test to use.
ps. In case this information is needed: I am using R to analyse my data.
