I am dealing with the following data frame:
GroupID Month Year Concession_10Yr Action Historical_Action_5Yr XY1 12 1984 1 0 0 ...
GroupID is a unique identifier for an organization. Then we have data every ear and month on whether the organization took "action", whether there was a "concession" (think "treatment") to this organization over the past 10 years and then whether the organization took action over the last 5 years.
What I'm trying to do is model the probability of an organization taking action. Clearly I can't do a simple linear regression as the organizations would be correlated with one another as some organizations are more likely to take action then others.
As such, I was thinking of a non-linear mixed effects model, something like:
lmer(Action ~ GroupID + Historical_Action_5Yr + Concession_10Yr + 1/GroupID/Concession_10Yr + 1/GroupID/Historical_Action_5Yr, family = binomial(link = logit)
However, I am not sure that I have the nesting correct. Also, this doesn't account for time directly...
Any guidance would be much appreciated!