3/9 Using undirected, weighted #social_networks, #GLMM, and behavioral observation.
The authors modelled the effect of #urbanisation on the social behavior of 6 populations of lizards, in an highly urbanized tourist destination and a coastal area with forest.

(All figures are from the articles)

Introduction to Generalised Linear Mixed Models for Ecologists | PR Statistics A five day online course introducing linear models, GLMs, and multilevel models for ecological data using R, lme4, and rstanarm. Learn regression, ANOVA, interactions, diagnostics, overdispersion, zer...

GLMM for Ecologists course 2-6 Feb.

A practical introduction to building mixed models in R. Taught by Dr Andrew MacDonald, a statistician focused on connecting ecological theory with expressive statistical models

prstats.org/course/intro...

#GLMM #Ecology #RStats #MixedModels #DataAnalysis

#statstab #450 Fitting GAMs with brms

Thoughts: Assuming linearity of your continuous predictors is not needed when you can add wiggles!

#gam #glmm #linearmodel #modelling #brms #rstats #bayes #tutorial #splines #r

fromthebottomoftheheap.net/2018/04/21/f...

Issues | Mixed Models with R This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mi...

#statstab #401 Common issues, conundrums, and other things that might come up when implementing mixed models

Thoughts: GLMMs are cool, but come with their own quirks.

#glmm #lmer #brms #mixedeffects #hierarchicalmodels #r

m-clark.github.io/mixed-models...

Introduction to Generalised Linear Mixed Models for Ecologists | PR Statistics Introduction to Generalised Linear Mixed Models for Ecologists (MMIE01) teaches the theory and application of LMMs and GLMMs using R. Participants model hierarchical ecological data with tools like lm...

PhD students: working with repeated measures, nested designs, or hierarchical ecological data?

This course teaches you how to model it properly using GLMMs in R.

Taught live by Dr. Niamh Mimnagh.

Sept 22–26

www.prstats.org/course/intro...

#RStats #GLMM #PhDLife #Biostats #Ecology #OpenScience

After Nickel was eliminated, Baseball, he… he lost his spark. And became… emotionless. 💔 #heartbreakinggachalifestory #glmm

Venn diagram representing the proportions of variance components in a mixed model.

【💡High Cited 2020-2022 】
glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models

#CommonalityAnalysis | #FixedEffect | #GLMM | #HierarchicalPartitioning | #RelativeImportance | #VariancePartitioning

doi.org/10.1093/jpe/...

One of My favorite gacha life lesbian series... Gone...😭
#gachalife #glmm

Pretend the chair is there🥲

Theses r my fav characters

I made a fanart of @/Joinen on YouTube's first gacha mini movie.

I highly recommend watching it. Here's the link🖇️ youtu.be/SDuuNcAtKxY?...

#gacha #gachaart #fanart #art #digitalart #gachaminimovie #glmm #gachalife

Need some stats & methods for your ecological/biological dataset?
Alain Zuur & Co. is coming to #DK - Introduction to #GAMM and #GLMM with R. #DTU, Lyngby, Denmark. 10-14 Feburary 2025.
#ecology #statistics #biology
You can join the course in person or online here: www.highstat.com/index.php/jo...

Restricted maximum likelihood estimation in generalized linear mixed models Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces less biased…

Extremely nice review of REML estimation for generalized linear mixed models.
Covers a couple of important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM

Restricted maximum likelihood estimation in generalized linear mixed models Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces less biased estimates of the variance components. However, the concept of REML does not immediately generalize to the setting of non-normally distributed responses, and it is not always clear the extent to which, either asymptotically or in finite samples, such generalizations reduce the bias of variance component estimates compared to standard unrestricted maximum likelihood estimation. In this article, we review various attempts that have been made over the past four decades to extend REML estimation in generalized linear mixed models. We establish four major classes of approaches, namely approximate linearization, integrated likelihood, modified profile likelihoods, and direct bias correction of the score function, and show that while these four classes may have differing motivations and derivations, they often arrive at a similar if not the same REML estimate. We compare the finite sample performance of these four classes through a numerical study involving binary and count data, with results demonstrating that they perform similarly well in reducing the finite sample bias of variance components.

Extremely nice review of REML estimation for generalized linear mixed models.
Covers a couple of important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM
https://buff.ly/405IaB0

🎄This Is Not A Christmas Song (Gacha Music Video) {Christmas Special}🎄 YouTube video by Cyborg Puppy

Hello everyone! This is my first time promoting a video on Bluesky! Go check out a Christmas music video I made today!✨️

🎄New Video🎄
youtu.be/loCFxCvCX00?...

#Sonic #Sonicoc #Gacha #Gachalife2 #Musicvideo #Sonicthehedgehog #Gachamusicvideo #Christmas #Holidayspecial #GLMM #Original

DHARMa v0.4.7 #rstats package for #glmm residual diagnostics now available on #CRAN. Among other things, this version includes a new residual test for #phylogenetic correlation, as well as support for the #phylolm package. cran.r-project.org/web/packages...

RT: DHARMa v0.4.7 #rstats package for #glmm residual diagnostics now available on #CRAN. Among other things, this version includes a new residual test for #phylogenetic correlation, as well as support for the #phylolm package. Moreover, to make DHARMa more accessible for color-blind

#statstab #204 GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Thoughts: No clue what this package is does, but seems useful. Maybe someone can explain some use cases.

#glmm #gaussian #modelling #r #stats

drizopoulos.github.io/GLMMadaptive/

There is only 1 seat left for the #GLMM in R course in October: www.physalia-courses.org/courses-work...

Come work with us - I am looking to fill a 3-yr position for a statistical postdoc / scientific programmer to continue the development of the DHARMa #Rstats #CRAN package for #glmm residual diagnostics. Full job advertisement is here uni-regensburg.de/assets/biolo...