I am trying to model the adaptation of muscle activities (i.e. integral of electromyographic) across different
days (longitudinal study). For each subject (variable
sbjID), I measure the variable
emg for each gait cycle,
day of recording. I am using mixed-effect models as follows:
emg.lm <- lme(emg ~ days*muscle, random = ~1|sbjID, data=data_emg, na.action=na.omit, method = "ML")
For the moment, I am modeling
days as a factor. The variable
muscle is a factor too.
I have two questions:
There are different numbers of gait cycles across days, and also across subjects. Does this unbalance design create issues in the context of mixed-effect models (e.g. bias towards groups with the higher number of cycles)? If that is not the case, you have reference where I can read the theory about it?
There is a certain variability in
emgacross gait cycles even within the same day, muscle and subject. It needs to be noticed that gait cycles of each subjects are consecutive, and therefore potentially correlated, random events. Do you think I need to take this into account in my model, and how? I am not interested in studying the influence of the gait-cycle sequence on
emg(and that's why I have not included as fixed-effect), but I wonder whether, for example, I should include this variable as random effects somehow (e.g. nested withing subject). Any help is greatly appreciated.
Thanks a lot!
EDIT. A person who commented this post told me that since I am treating
days as a factor, then I should have random slopes, otherwise I will inflate the estimation of the standard errors. I have additional two questions in these regards:
I have read this type of comments on other posts, but I am not sure I understand it. Can someone provide explanation for it?
daysis a random effect, the interaction term
days:musclewill also be a random term. Isn't this equivalent to fit a different model for each subject (and therefore not providing any structure)? Sorry if this question is naive, I am just starting using linear mixed effects, and I am trying to understand.