3 votes
Accepted

Linear mixed effects modeling with MZ and DZ twin pairs

Comments: To understand the statement "This step allows the method to be generalizable to other cohorts since we are treating the individuals as if they were unrelated.", consider Figure 1 ...
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  • 2,156
2 votes

Random Effect in twin study of both MZ and DZ twin pairs

I answer your question about the linear mixed model(s) for microbiome gene/species abundances by New et al. described in [1]. You already asked a very similar question here. In the New et al. paper, ...
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2 votes
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How to deal with crossed and nested factors at the same time in a linear mixed model?

If I understand correctly, Event represents the type of event identified to the investigator (personal versus news story, independent of the particular nature of ...
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1 vote

Multilevel model for education data, if individuals within cluster change (school) between time points

I'm fairly sure this is a classic case of multiple membership, and there are models that have been proposed to account for things like this: https://arxiv.org/abs/1907.04148#:~:text=Multiple%...
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  • 425
1 vote
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question of a Poisson random effect model

Start with your first expression: $$\log E(Y_{i}|b_i,x_i)=\beta_0+\beta_1x_i+b_i$$ The effect of a one-unit change in $x_i$ is easy to write: $$\log E(Y|x_i+1,b_i) - \log E(Y|x_i,b_i) = \beta_0+\...
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1 vote
Accepted

Can causal relationships be inferred from a random effects panel model?

I would say, yes, causal relationships can be inferred from random effects panel models under certain circumstances, especially random assignment of a treatment. Unfortunately, I would say no, you ...
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  • 130
1 vote

Why is there heteroskedasticity even though no relationship seems present in the residual plot?

First, focus onthe first plot, without outlier removal: To me it is clear that the spread of the points around the horizontal zero line is increasing with increasing fitted values! If you calculate ...
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1 vote

How is the standard deviation of random effects estimated?

Suppose that your model is something like $$ y_{ij} = \beta + u_j + \varepsilon_{ij} $$ where $u_j \sim \mathcal{N}(0, \sigma^2_u)$, for $j=1,\ldots,m$, are the random intercepts and $\varepsilon_{ij} ...
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