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Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.
4
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GLMM and BLUPs: high correlation between random effects in a logistic GLMM
Background:
In an experiment, subjects had to choose whether they wanted an immediate reward or to wait for a larger reward (dichotomous dependent variable: yes/no). This choice was made multiple time …
3
votes
1
answer
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Why not us conditional modes (BLUPs) for further analyses?
De Waters et al. (2017 ;2019) used conditional modes (BLUPs) from a generalized linear mixed model to further examine individual differences. In their experiment, they performed a temporal delay task …
2
votes
0
answers
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glmmTMB & glmer: diffrences in results and warnings of a logistic glmm
I was fitting a logistic generalized liner mixed model using glmer from the lme4 package when I stumbled upon the fact that the results change drastically when the order of the data was changed. So I …
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answers
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Bootstrap for a model with crossed random factors in R (bootMer)
I want to bootstrap my model with two random factors, subject and item, which are crossed. I have specified my model as follows:
lmer(outcome ~ predictor_level1 + predictor_level2 + (1 | subject) + (1 …