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mixed effect models

1. random effects

  • As I understand it, these are effects that occur at the group level. So you can imagine that in a given hospital, you want to plot the outcome of each patient. The outcomes are effected by the drugs they take. But each doctor results in a different y-intercept for the group of patients that they are treating.
  • random effects:
    • the effects might change depending on how you did the experiment
    • for example, the doctor effect should not be a fixed effect, since it was up to chance which doctors would be included in the experiment
    • we assume that the doctors included in our study are drawn from a pool of doctors, which has its own distribution that can be estimated
    • in this way, we can think of random effects a little like nuisance variables
  • basically, random effects are there to avoid the problem of treating samples like they are all drawn from the same pool
    • when in reality, they are drawn from many pools, where each pool is fairly homogeneous
  • my favorite stack overflow answer

2. colin conwell's explanation

  • random effect is something that we don't build into the model, but we still want to model it
  • there are group effects that we want to model
  • important where differences between individuals (groups) are important
    • so you can fit a random slope and/or a random intercept to make assumptions about where individuals start with respect to the target, and where they will end up based on treatment

3. lmer

4. my questions

5. useful links

Created: 2025-11-02 Sun 18:54