Due to a recent discussion with colleagues on whether and when to use #LinearMixedModels (#LMM), I wrote a blog post comparing LMM to other approaches using simulated data. I thought, it may also be useful for others working with hierarchical data structures in #neuroscience and beyond.
🌍 https://www.fabriziomusacchio.com/blog/2026-01-31-linear_mixed_models/
#Python #Statistics #DataScience #MixedModels #Statsmodels #ANOVA #ANCOVA #GLMM #regression
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#freeonlinelearning #colisty #courselist #sasandrintegration #sas/iml #sas/stat #dataanalysis #statisticalmodeling #matrixalgebra #logisticregression #anova #mixedmodels #programmingwithsas #rtosas
https://colisty.netlify.app/courses/sas_-programming-for-r-users/
Hello Everyone! I have been experimenting with using #Quarto to call #julialang to run #multilevel models with #MixedModels. Unfortunately, my document is taking about 10-15 minutes to render with small data sets. I've found it difficult to understand the #julialang documentation on this issue, so would appreciate any "Explain to me like I'm 5" explanations of how to speed up #julialang. Code is here: https://agrogan1.github.io/multilevel-multilingual/. I am grateful for #julialang, just wish I could figure out the speed.
New on the blog: showcasing the immense hackability of #brms by extending a random intercept model with linear predictors on the standard deviation of the random intercept. Should you do it? Most likely not, but if you really really want, there is a way. Also the techniques shown are general and let you do a lot of other crazy stuff with brms. Happy for any feedback!
https://www.martinmodrak.cz/2024/02/17/brms-hacking-linear-predictors-for-random-effect-standard-deviations/
#bayesian #BayesianStatistics #BayesianInference #MixedModels
Exciting News! 📚 Our work on Reliability and Feasibility of Linear Mixed Models in Fully Crossed Experimental Designs published in AMPPS! 🎉 #R #lme4 #MixedModels @Scandle & @letstido @universityofleeds
https://journals.sagepub.com/doi/10.1177/25152459231214454
We present #recommendations and a clear #pipeline for handling #random effects in the presence of non-convergent and singular models. No more reduced models causing first-type errors due to data pseudoreplication!
Recent #PeerReview work on #MixedModels and #GrowthModels had me going back to these classical texts on the genre:
1) https://journals.sagepub.com/doi/10.3102/01623737018004265
They are excellent tutorials on the most basic aspects of incorporating trajectories / functional change into such models. And the assumptions these require.
Another #PeerReview finished.
Paper ~ 7000 words
Review ~ 2000 words
Duration ~ 2 hours
I notice a long manuscript less, if it is well-written. Main point here once again:
#MixedModels are difficult to report. This paper offers a lot of detail on how to develop such a project and report it (especially Table 7): https://www.sciencedirect.com/science/article/pii/S0749596X20300061
The chapter in Hancock & Mueller's "The reviewer's guide to quantitative methods in the social sciences" is also very helpful.
I often look at papers where authors used a lot of effort to shoehorn a #LongitudinalAnalysis into a trajectory or #MixedModels that do not quite the job the team wants.
Analysing longitudinal data (esp. w time-varying covariates) via G-Estimation is an alternative for consideration:
https://journals.sagepub.com/doi/full/10.1177/25152459231174029 #Tutorial
The underlying thinking is not entirely different, but often one needs only a little step / laterality to get a new view on an analysis problem.
Another #PeerReview finished.
Paper ~ 7000 words
Review ~ 2000 words
Duration ~ 2 hours
You notice a long manuscript less, if it is well-written.
Main point here once again: #MixedModels are difficult to report.
This paper offers a lot of detail on how to develop such a project and report it (especially Table 7): https://www.sciencedirect.com/science/article/pii/S0749596X20300061
The chapter in Hancock & Mueller's "The reviewer's guide to quantitative methods in the social sciences" is also very helpful.
Another #PeerReview finished.
Paper ~ 4700 words
Review ~ 1500 words
Duration ~ 2 hours
The application of #MixedModels requires discussion of the decisions made in modeling as well as detailed reporting of a range of results.
This paper offers a lot of detail on how to develop such a project and report it (especially Table 7): https://www.sciencedirect.com/science/article/pii/S0749596X20300061
Unfortunately it is not #OpenAccess and no alternative version seems to be available 🤓
New Blogpost on Type-1 error in LMM/MixedModels
What happens if one does not include random-slopes?
https://benediktehinger.de/blog/science/lmm-type-1-error-for-1condition1subject/
Including an Interactive Demo: https://benediktehinger.de/interactive-pluto-notebooks/output/lmmType1_simple.html
this is mostly restating Barr et al. 2013 and making it freshly accessible - but I hope it helps!
If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
---
y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
---
If this sounds relevant to you, I could prepare a blog-post + interactive demo
Take a weird dive into the Intraclass Correlation Coefficient (ICC) with my newest statistics meditation! 💖🤓🌌
How can the ICC be a correlation and a proportion of variance at the same time? Zone out to this question, the chickens, and the roosters. 🐓🎧
This is probably most interesting to you if you are already mildly motivated to think about the #ICC. #Statistics #Meditation #IntraclassCorrelation #MixedModels #MultilevelModels #Correlation #VarianceComponents #STEAM
Finally working to wrap my head around time-varying predictors. Read Wang & Maxwell 2015 today, which discusses detrending X and Y in #mixedmodels to remove the effects of time. Is this actually appropriate for the context of #aging research?
Time for an #introduction:
I’m Ewan 👋 a statistician and researcher in Biostatistics & Health Informatics, King’s College London. I do things with numbers and mental health.
Right now, that includes: (1) mental-physical links in routine data; (2) wearable sensors to predict depression; (3) clinical trials of digital interventions.
I’m excited about #OpenScience, #Bayesian (#brms), #MixedModels, #rstats, #tidyverse, #Quarto, #DAGs.
Elsewhere: gravel cycling, ⚽️, ☕️, breaking things in #Linux.
A nice example of replication in social science: Age and sex effects of third-graders' physical fitness in two German states. #EMOTIKON #bekigeki #MixedModels #JuliaLang #rstats #osf
Two #rstats things that people might (hopefully) find useful are for example this blog I wrote about #poweranalysis through #datasimulation https://julianquandt.com/#posts (covering t-test / anova / linear mixed models and more) and these standard operating procedure for #lmm #mixedmodels (#brms and #lme4) I took part in creating http://decision-lab.org/wp-content/uploads/2020/07/SOP_Mixed_Models_D2P2_v1_0_0.pdf
Interested in role of social structure for children's physical fitness and mixed-model statistics
#EMOTIKON (https://www.uni-potsdam.de/de/emotikon/index )
#BeKiGeKI (https://www.bekigeki.de)
#MixedModels with #RStats (lme4; https://github.com/lme4/lme4/) and #julia (https://github.com/JuliaMixedModels)
#OpenResearch advocate since 2006 ( http://read.psych.uni-potsdam.de/pmr2/ ). Recent stuff on #OSF (https://osf.io/9ivha/)
