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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 votes
0 answers
280 views

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 …
バシル's user avatar
  • 133
3 votes
1 answer
173 views

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 …
バシル's user avatar
  • 133
2 votes
0 answers
787 views

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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  • 133
1 vote
0 answers
82 views

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 …
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  • 133