Dear Cross Validators,

I have a data set of the following structure (don't mind the numbers):

subject_ID Position_of_the_measure Environment_condition Variable_of_interest
1 A 10.3 40
1 B 10.3 36
2 A 15.2 62
2 B 15.2 48
3 A 21 27
3 B 21 29

I want to investigate the effects of the Position_of_the_measure and environment_condition on my variable_of_interest.

Obviously the values are non independent (as paired by ID) so I figured linear mixed models would have been the go to with ID as random effects but I don't have multiple measures per ID per Position (this should have been thought in the sampling design but here we are).

I wonder if you guys have an idea about how to deal with this from there (working with A-B might work but I'd prefer not to).

I know that I'm pretty much asking for what could be considered as statistical non sens but wanted to know if I had miss something obvious?

Thank you for your time.

Here is an example of plotted data which might help you understand: enter image description here



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