Timeline for Increasing multicollinearity in multilevel/hierarchical modeling?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Jul 24, 2020 at 7:40 | history | edited | Robert Long |
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| Jan 8, 2020 at 22:21 | comment | added | hatmatrix | (Correct about the "non-nested" part, but there is no intercept.) | |
| Jan 8, 2020 at 15:44 | comment | added | hatmatrix | @Erik Ruzek yes that's correct - thanks for the links! | |
| Jan 8, 2020 at 9:00 | history | tweeted | twitter.com/StackStats/status/1214834262173782016 | ||
| Jan 7, 2020 at 14:54 | comment | added | Erik Ruzek | Is it that you are interested in a "non-nested" model, in which you have (very) many groups and you want to allow each of these groups to have their own random intercept? See lme4.r-forge.r-project.org/book/Ch2.pdf and vulstats.ucsd.edu/pdf/Gelman.ch-13.more-multilevel-models.pdf. | |
| Jan 7, 2020 at 11:57 | answer | added | Robert Long | timeline score: 3 | |
| Jan 7, 2020 at 0:40 | comment | added | hatmatrix | The physical model specifies that all variables $x$ are multiplied by coefficient $b$ to produce $y$. However, to estimate these coefficients it's not clear that slopes for all variables will vary if the subgroups $g$ become increasingly large relative to the number of samples (currently targeting about 10:1 sample to group ratio). | |
| Jan 7, 2020 at 0:12 | comment | added | Erik Ruzek | Just to be clear, do you imagine that the slopes for all variables $X$ are allowed to vary across groups $g$? Put differently, are all predictors specified to have random/varying slopes? And how many groups were you thinking of? | |
| Jan 6, 2020 at 23:32 | history | asked | hatmatrix | CC BY-SA 4.0 |