Timeline for Detecting Random & Crossed Random-Effects from Model Syntax in R (lme4)
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Feb 4, 2021 at 5:40 | comment | added | rnorouzian | Dear Robert, HERE is a question that might be of interest to you. | |
| Dec 4, 2020 at 2:59 | vote | accept | rnorouzian | ||
| Dec 3, 2020 at 15:51 | comment | added | Robert Long | @Reza Sorry but I don't follow you. The question specifically asks about infering whether factors are nested or crossed, from the model formula. A model cannot inform the modeller about the nesting or crossing of factors. If you already know whether the data are crossed or nested, then all that is needed is to specify the random structure appropriately. Alternatively the wrong structure can be specified, and invalid inferences potentially obtained. However the formula will not provide any insight about the study design. | |
| Dec 3, 2020 at 15:40 | comment | added | Reza | Thank you Rob, but notice that the question intentionally is saying that assume that model syntax and the data "precisely represent the study design by a researcher". That is, there is no ambiguity whether the model syntax matches the data structure or the study design or not. Also, the linked blog post clearly shows the study design for each model syntax. So at its core, the question is how the random part of each model syntax can support is corresponding study design. In another question I will directly share the design and model syntax and ask how the syntax supports the design. --Thanks. | |
| Dec 3, 2020 at 11:44 | history | answered | Robert Long | CC BY-SA 4.0 |