Timeline for Mixed models: Assessing significance of random effects
Current License: CC BY-SA 4.0
8 events
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| Mar 25, 2021 at 12:26 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
added 244 characters in body
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| Jul 10, 2018 at 15:28 | comment | added | Heteroskedastic Jim | You're right about model 1. You can interact crossed factors. Think standard analysis of variance. | |
| Jul 10, 2018 at 14:41 | history | edited | Seraf Fej | CC BY-SA 4.0 |
Further clarification of question
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| Jul 10, 2018 at 14:35 | comment | added | Seraf Fej | @user162986 In that case would model.1 not be the most extensive specification in that case as air has slopes and intercepts for both of the random effects? The linked blog post seems to indicate (see "Our Second Mixed Model") that interactions don't make sense for a crossed design (although it could well be my interpretation that is wrong). ourcodingclub.github.io/2017/03/15/mixed-models.html | |
| Jul 10, 2018 at 13:58 | comment | added | Heteroskedastic Jim | When you add more terms to a model, the model becomes more complicated, so extensive specification. By makes sense, I mean justify them in some way. The claim is that a coefficient is different depending on the group the case belongs to. And you can have the interaction with crossed factors too. | |
| Jul 10, 2018 at 13:45 | comment | added | Seraf Fej | Thanks for the response @user162986. What exactly do you mean by extensive specification? From what I read I got the impression that using ID:Session would make sense if they were nested rather than crossed? How is it determined if these specifications make sense exactly, is it just a matter of stating some justification for them? | |
| Jul 10, 2018 at 12:35 | comment | added | Heteroskedastic Jim |
If a random effect has a standard deviation that is actually zero, it is likely that all of its covariance with the outcome is modeled by another term in the same model. Model.3 is the most extensive specification you have. If the grouping variables are crossed, it's possible you could additionally have (1 + Predictor | ID:Session). If all these specifications make sense and you have sufficient information in the data to estimate them, you can attempt the more flexible specifications. In your example above, you can't do this as the interaction of ID and Session is a single data point.
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| Jul 10, 2018 at 11:45 | history | asked | Seraf Fej | CC BY-SA 4.0 |