#mixedmodels — Public Fediverse posts

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

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

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

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

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

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

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

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

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

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

2) https://www.cambridge.org/core/journals/development-and-psychopathology/article/design-and-analysis-of-longitudinal-studies-of-development-and-psychopathology-in-context-statistical-models-and-methodological-recommendations/3F89776EE1F27BA4E4F49435AFBB7662

They are excellent tutorials on the most basic aspects of incorporating trajectories / functional change into such models. And the assumptions these require.

#HLM #History #SurvivalAnalysis

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.

#MultilevelModeling

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.

#MultilevelModeling

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.

#MultilevelModeling

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.

#QuantitativeMethods #Rstats

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.

#QuantitativeMethods #Rstats

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.

#QuantitativeMethods #Rstats

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.

#MultilevelModeling

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.

#MultilevelModeling

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.

#MultilevelModeling

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.

#MultilevelModeling

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.

#MultilevelModeling

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!

@cogsci @cognition @eeg #statistics #MixedModels

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

#statistics #LMM #MixedModels #julialang

Take a weird dive into the Intraclass Correlation Coefficient (ICC) with my newest statistics meditation! 💖🤓🌌

https://youtu.be/PqFJ2cggFfY

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