Hello!
I am trying to analyse data from animal behaviour.
4 groups (treatments) with 10 animales each.
4 different activities recorded: Sleep, walk, eat, play.
I want to know if any treatment cause a different proportion of the activity in each group.
Do someone can help me with this? I need a script to run in SAS. I am using sas for student.
Example. The data is the frequency of each activity recorded per animal in a period of 2 hours.
Group | 1 | 1 | 1 | 2 | 2 | 2 | |
Animal | 1 | 2 | 3…(10 animals each group) | 1 | 2 | 3…10 | |
Sleep | 15 | 10 | 16 | 16 | |||
Walk | 23 | 14 | 23 | 23 | |||
Eat | 14 | 22 | 24 | 24 | |||
Play | 26 | 36 | 54 | 25 |
Thank you.
Please supply some example data in a data step with datalines, and an example for the result you expect.
If you want to use your existing dataset (or a subset thereof), use the macro provided in https://communities.sas.com/t5/SAS-Communities-Library/How-to-create-a-data-step-version-of-your-dat... to convert the dataset to a data step.
Post the resulting code in a code window, as described in https://communities.sas.com/t5/help/faqpage/faq-category-id/posting?nobounce.
Thanks! I am trying to to what you have suggested me, but I have being experimenting some problems.
Just in advance, is proc glimmix categorical analysis suitable for data of behaviour?
Regards.
Don't look for a PROCEDURE, decide the type of statistical analysis you need first. And then someone can either tell you the PROC that will work or you can search for that.
Hi Reeza, How are you? I hope everything is fine.
To choose the type of statistical analysis I need, these are some point to take into account:
I need a test to address the question is there a difference between groups? (independent groups.) Comparing between groups.
10 collection time points.
Independent variables: Categorical data (nominal).
Dependent variables (treatment): Categorical data (nominal).
4 Treatments.
Data with normal distribution (parametric tests)
Can be the Chi-Square X2 test suitable to be used in this situation?
All the best.
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