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Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.

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The broom (broom.mixed) package allows you to automatically compute the subject-specific predictions with the augment function (fm_augmented <- broom.mixed::augment(fm).) Edit in function of your comm …
answered Jul 20 '21 by KrisBae
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Generally speaking, it is no problem to get 'overall' parameters using mixed models. Typically, in this case I think you would take up the regions as a random effect and the overall predictor variable …
answered Jul 23 '21 by KrisBae