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Late to this party but didn't want to miss out on all the fun! The terminology of level-1 and level-2 predictors is usually reserved for a situation where you have two random grouping factors (e.g., Industry, Company) and one of those factors is nested in each other (e.g., Company is nested in Industry, in the sense that each Industry represented in your ...


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I realized that my comment above is general, so I'll submit it as an answer: In lme4 syntax, fixed effects are entered as you have them here, and random effects are specified with the | operator. Correctly specifying the random effects is key to returning the correct fixed effect coefficient. It looks like you have specified the nesting of companies in ...


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Since this question is about re-writing a model specification, I'll focus on that aspect. Without knowing more about the data or precise inferential goal, I can't comment on whether either model is the right tool. I'm also unsure why the OP needs to rewrite a functioning model to use a different function within the same software package. Let's start from the ...


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The problem here is that you are fiting random intercepts for condition. Since you are interested in testing this variable, you should fit fixed effects for it. Moreover, with only 3 conditions, it doesn't make sense to fit random intercepts anyway since you will be asking the software to estimate a variance for a normally distributed variable from only 3 ...


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If you alter the control paramenters in lmecontrol it converges: cl = lmeControl(maxIter = 200, msMaxIter = 1000, niterEM = 500, msMaxEval = 2000) two <- lme(value ~ 0 + name+ name:uerate, data = dat, random = ~0 + name+ name:uerate | id, weights = varIdent(form = ~1 | name), control = cl)


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Multilevel analyses do not assume that effects are fixed, they can be specified as fixed or random, but it is up to you to choose. I would regress $X$ on $Y$ and adding days as clusters. The nice thing about multilevel analysis is that it weights the effects of days. For example, consider two scenarios, you could either (a) pool your data per day (average by ...


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R package glmertree allows for fitting decision trees to multilevel and longitudinal data (which would otherwise be modeled with a mixed-effects model). It allows for specifying a random effects structure, and partitions the dataset into subgroups using level-I, or higher-level predictors. For further reference, see the package vignette (tutorial): https://...


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R package glmertree would be perfectly suited for this purpose. It allows for specifying a random effects structure, as well as specifying predictors that have to occur in the model, based on e.g., expert domain knowledge. This should be limited to a small set of predictors (1 or 2) though. For further reference, see the package vignette (tutorial): https://...


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