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I am using lmer for estimating effect sizes and I am comparing the results with estimations obtained through OLS. I first ran the LMM model with simulated data where the effect sizes are known and I get in both cases correct estimations(lmm model estimation is the same as OLS estimations). However when I run some real data through the lmm model, I get for certain effects different answers between lmm and OLS.

Both techniques use the same designmatrices, yet i fail to understand why I get different estimations between the two methods, is this normal?

Also, when I use the commands ranef on the lmm model, I obtain my random effects how do I verify that these are BLUPS?

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  • $\begingroup$ Could you possibly have some oddness in the distribution of your actual data? Or perhaps the LMM estimation had an issue with convergence? The ranef function will return BLUPs as explained further here: stats.stackexchange.com/questions/111702/… $\endgroup$ – kurtosis Aug 25 '20 at 0:47
  • $\begingroup$ No not really, I also scale and center the data in both situations. After that I plotted density plots of the variables and I can't reall see anything weird $\endgroup$ – Hedayat Aug 25 '20 at 5:18
  • $\begingroup$ What is the random effect structure? Maybe that has an effect? (I'm grasping a bit since I could see a difference in a GLMM but not an LMM, but... maybe more information will ring a bell.) $\endgroup$ – kurtosis Aug 25 '20 at 6:08
  • $\begingroup$ I am not sure if understand what you are asking, do you want to know how the distribution of the random effects look like? I do get the following warning when I run the lmm model "fixed-effect model matrix is rank deficient so dropping 1 column / coefficient" $\endgroup$ – Hedayat Aug 25 '20 at 8:17
  • $\begingroup$ That error is probably the source of your problems. $\endgroup$ – kurtosis Aug 25 '20 at 18:17
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I went back to my simulated data and added on top of the created effects some noise(e ~N(0,1)). After that I calculated my estimators in both ways (OLS and via LMM) the estimators are no longer identical for the interaction and random effects. So the cause of the error is the noise.

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