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What is mixed random effect model?
A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures analysis of variance.
What are nested models in R?
Two models are nested if one model contains all the terms of the other, and at least one additional term. The larger model is the complete (or full) model, and the smaller is the reduced (or restricted) model.
How are nested random effects different from crossed random effects?
Nested random effects occur when a lower level factor appears only within a particular level of an upper level factor. For example, pupils within classes at a fixed point in time. Crossed random effects means that a given factor appears in more than one level of the upper level factor.
When does a random effect cross a hierarchy?
As you can see, random effect is crossed if it appears in more then one of the higher hierarchy levels.
When do nested random effects occur in lme4?
Nested random effects occur when a lower level factor appears only within a particular level of an upper level factor. For example, pupils within classes at a fixed point in time. In lme4 I thought that we represent the random effects for nested data in either of two equivalent ways: (1|class/pupil) # or (1|class) + (1|class:pupil)
Is there a third level of crossed random factors?
A third level is possible as well. This would happen if each doctor sees all their patients at one of four hospitals or each field has only one of 5 species. In one kind of 2-level model, there is not one random factor at Level 2, but two crossed factors. Each observation at Level 1 is nested in the combination of these two random factors.