var(ranef(Random Effect)) not the same as the variance component

On 9/7/20 9:22 PM, Simon Harmel wrote:

Ben,

This might seem irrelevant to my previous question, but it is from the
post you linked in your previous answer. So, is it correct language to

say:

By including Random-Effects (e.g., random intercepts) of some
subjects, we are **controlling/adjusting/holding constant**
subjects's random variations in that random-effect (e.g., variation in
subjects' initial status)?

    I would probably say something like "incorporating among-subject
variation in that term (e.g., the initial status) in the model". Or
"accounting for".

On Mon, Sep 7, 2020 at 7:53 PM Simon Harmel <sim.harmel at gmail.com
<mailto:sim.harmel at gmail.com>> wrote:

    Much appreciated, Ben. I will study those resources to better
    understand the estimation process.

    Thanks again,
    Simon

    On Mon, Sep 7, 2020 at 6:25 PM Ben Bolker <bbolker at gmail.com
    <mailto:bbolker at gmail.com>> wrote:

            Yes, that's correct.

          From

https://stats.stackexchange.com/questions/392283/interpreting-blups-or-varcorr-estimates-in-mixed-models/392307#392307

          > the covariance matrix of the empirical Bayes estimates

        obtained from
        ranef() is related to the covariance of this posterior
        distribution [of
        conditional modes/BLUPs] whereas VarCorr() is giving the D
        matrix, which
        is the covariance matrix of the prior distribution of the random
        effects. These are not the same.

        On 9/7/20 7:22 PM, Simon Harmel wrote:

         > Hello All,
         >
         > A very basic question. Generally, `var(ranef(Random Effect))`

        may not

         > necessarily be the same as the variance component reported

        for that Random

         > Effect in the model output, correct?
         >
         >
         > Thank you all,
         > Simon
         >
         >       [[alternative HTML version deleted]]
         >
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