Hello,

I am using mixed effects models of the following form, see below.

reaction_time ~ x + y + z + (1 + x + y | participant)

Is there a way to get BLUPS here to predict the random effects using brms?

Hello,

I am using mixed effects models of the following form, see below.

reaction_time ~ x + y + z + (1 + x + y | participant)

Is there a way to get BLUPS here to predict the random effects using brms?

Perhaps the `coef()`

or `ranef()`

functions will get you what youâ€™re looking for.

Are the above functions just returning the random effect value sampled from N(0, Sigma) or are the above functions actually estimating the effect on a particular participant?

If the latter, then how accurate is the estimation using coef() of a mixed effects model compared to a fixed effects with model with interaction terms such as x:participant and y:participant, which of course would create an extremely complex model and is not feasible in my case since I have 200+ participants?

ranef() gives this.

coef() gives the population-level effects (a.k.a. â€śfixed effectsâ€ť)

Yeah, donâ€™t treat something with 200+ levels as a fixed effect. And your wording implies you think such a model will be more â€śaccurateâ€ť, but this is not the case. I donâ€™t have the links handy (afk atm) but search for the case studies on â€śpartial-poolingâ€ť for how you can achieve greater accuracy by treating random effects as such.

I believe @mike-lawrence is a bit off, on this. With **brms**, the `fixef()`

function is how you get the fixed effects. `ranef()`

returns the participant-specific estimates in the deviance metric. `coef()`

, however, returns the participant-specific estimates in the metric of the fixed effects plus the participant-level deviations.

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Ah! Thatâ€™s embarrassing; Iâ€™ll have to remember to double-check myself before commenting on brms stuff. Thanks @Solomon for the correction!

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Cheers!

Hi @Solomon , thanks so much for your help here, just wanting to check if you agree that above is the correct interpretation of ranef() and coef()'s ouput.

Yes, `ranef()`

and `coef()`

both give the participant-specific effects, just in different metrics.

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Thank you!

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