Today I noticed that the AIC values included in the model table when I do a likelihood ratio test of two LME models do not match the AIC values I get for those models if I use the AIC() function. Is this common knowledge? Is there a good reason (e.g. something from math stat I've forgotten that makes these questions not the same)?

Here's a code snippet demonstrating. https://gist.github.com/emjonaitis/237402a6d5c6e9338aeebebbd121eba3

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!

What model #statistics should one report after using multiple #imputation and #multilevel regressions, and how are they obtained? I'm using the #mice package in #rstats, and #lme4 on each imputed dataset. When pooling results, summary() yields what I need for each model term, but nothing for the whole model. If I didn't impute but deleted listwise, I would normally report AIC, BIC, Loglik. These are all in the mipo object, for each result for each imputed dataset, but they're not pooled. I'm sure I'm missing something here. Does anyone know an example article where such results are presented neatly?

Hey #mixedmodel #rstats peeps - anyone know of a package or function that does for #glmmTMB what merTools::predictInterval() does for #lme4? #wantingToMoveEverythingOver

I have written a book draft of an introduction to #multilevel modeling, entitled #Multilevel Thinking: https://agrogan1.github.io/multilevel-thinking/. Comments, questions and corrections are appreciated, as are suggestions for a possible publisher.

While applicable to many different software programs, the book is currently centered around the use of #Stata, but I hope to extend it to use of #rstats (#lme4) and #julialang

A mixed effects question for you #rstats #lme4 people: Persons are organised in groups for some time. No movement between groups. We have self-ratings of a Trait, and all persons in the group rate all other persons in the same group on a Behavior (but larger n/groups than in the picture). I want to predict Behavior by Trait. My first thought is a linear mixed-effects model with two random terms: Behavior ~ Trait + (1 | Subject) + (1 | Rater) Would that work? This is new territory for me.

What's the standard package in Python for doing #MixedEffectsModels instead of #R? I need to use Python unfortunately but want something with #lme4 / #lmer / #brms functionality

Our research group is doing a seminar the coming week, and I've been asked to hold a one-hour intro to quantitative diary studies and #multilevel analyses for new #phd students unfamiliar with this way of collecting data. I'd like to highlight benefits, when it's appropriate, show an example using #rstats and #lme4, and start a discussion around how they can do this themselves in their projects.

Does anyone have suggestions for stuff to include, or clever ways to convey it? Resources?

Do any of you guys have a good example of application of mixed models, on a publicly available dataset? #rstats #lme4 #lmm #linearMixedModel