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Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).

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I'm looking at the amount of carbon in seven forest pools. For dead trees left on the landscape across many locations and over several harvest retention (logging) treatments, there is an extreme value ...
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I'm analyzing data on daily foraging dynamics of animals in different treatments feeding on a diet consisting of two different qualities (high and low) using R. The problem arises when there are days ...
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I'm analyzing an ecological dataset of nutrient concentrations (continuous) across seven stations (each station is nested within one of three sites). We also have ~60 samples from each station where ...
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I'm running an experiment where subjects need to determine if a test-image is identical or different from their (memorized) target-image. The images are divided between categories (e.g. ...
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Problem Description: I want to fit a generalized linear mixed-effects model with a binary response (i.e., a Binary logistic mixed-effects model) where there are nested random effects, and each nested ...
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I'm working on a dataset of ~2900 fish, where the visually estimated sex was compared to the true sex. In about 10% of the cases (≈260 fish), the estimation was wrong (deviation = TRUE). I'd like to ...
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I built a generalized linear mixed model using the code: ...
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I am currently in the process of trying to complete a poisson GLMM analysis with two fixed (with an interaction) and two random effects using the glmer() function ...
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I am trying to construct a zero inflation Poisson GLMM using MCMCglmm(). I am new to Bayesian Statistics and this function and I am struggling to understand a couple of things. For my data I am ...
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When doing post-hoc treatment comparisons with the results from a glmm it is typical in my industry to use PROC GLIMMIX, method=rspl, ddfm = kr, and whatever control method is appropriate (ex. Tukey, ...
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I have made an analysis to test whether the weight of a mice population has changed between two periods. Data have been collected in the period 1978-81 and 2005-07. Many mice were captured through the ...
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I'm running GLM(M)s on proportional data ([0,...,1] ) using a binomial family and weighted to number of trials. ProportionFlowertoPod_Site.b = glmmTMB(PropFlowtoPod ~ Site_ID, family = binomial, ...
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I contact you because my case is particular and I don’t know much about GLMM. I have data of social networks (network metrics) of a nonhuman primate species. These data are by nature non independent (...
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I have a dataset with 72 individuals from 5 separate groups, with repeated measures - each individual was sampled 4 times. All data is binomial, and in most cases there are more 0s than 1s. I have a ...
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How do I choose a good model for this analysis? I'm going to describe the scenario below, and outline several options I have brainstormed. First, the scenario: I have data from 50 technicians that ...
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I want to predict the relationship between the number of groups a person belongs to and their overall well-being (totalwell). However, I would like to consider the possibility that a person could ...
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I have measurements of resin production of pine, which are taking tapping the tree, that is, making a physical wound and collecting the resin in a pot. When the pot is full we replace it with an empty ...
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I am running an analysis of a pre-post cluster RCT in a GLMM framework. Why does LMER include cases with only post data, but not the cases with only baseline data? Is there a way I can make it use the ...
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I’m fitting a two-level logistic mixed model with a random intercept and only level-1 predictors. The data are highly unbalanced across clusters: 266 observations in 25 clusters with sizes like: ...
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As the question title says, I am confused why the estimated marginal means (obtained using emmeans()) for a Bayesian binomial generalized linear mixed model are so ...
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I am fairly new to more complex statistics and I'm trying to get my head round appropriate variable selection methods including Lasso shrinkage, so would really appreciate any help and guidance ...
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This question is related to the selection of appropriate model strategy. My dataset has 2500 rows of district-level data of disease counts (number of cases). The response variable is number of cases. ...
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I got stuck interpreting the result of a generalised linear mixed model (GLMM). Feedbacks on how to compare two coefficients within a categorical fixed effect would be really helpful! To be specific, ...
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I have a binary repeated measures outcome with rare events. In particular, when comparing the outcome between different groups, sometimes the Odds ratios can blow up to infinity due to sparsity/rare ...
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My understanding of this topic has been cobbled together from various package vignettes (e.g., here and here) and other stackexchange posts (e.g., here). The information therein has been very helpful, ...
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I am comparing several models using PROC GLIMMIX, and many of my models are coming out with the exact same AIC value, even when new variables are added. For example, the code below yields the exact ...
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I have a dataset that contains information on purchases (in euros), salary, and other variables that reflect the purchasing preferences of each subject. The measures are repeated over time for each ...
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I would like to use weights in a model that I'm fitting with the glmmLasso package, but it looks like there isn't an option for it. I've previously fit models with ...
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I have a mixed effects model with a binomial outcome which I constructed using glmer from the lme4 package in R. In the output ...
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I used the R package glmmTMB to analyze a dataset using a binomial model and a hurdle model, then used the package ggeffects to generate predictions from both models. In glmmTMB, binomial models ...
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I am using glmmTMB and keep getting this diagnosis on my model when I check it: ...
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I am trying to decide on the best method for producing model predictions (for graphing) from my generalized linear mixed effects model. I am interested in getting marginal predictions (i.e., what the ...
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Study design: each subject chose between two feeders with different sweetness, the difference between the sweetness was the 'condition'. There was four levels of condition, and ten subjects per level. ...
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I am working on some modeling for relative abundance data. A previous iteration of this study was modeling species density and used GLMMs fit to a Gamma distribution with a log link. There's one main ...
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I have a dataset with repeated measures, different individuals each have six appointments in total. The outcome variable is continuous. I want to know if I should use a GLMM or a LMM to see what the ...
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I'm analysing the effect of various factors on the volumetric measurements of the human brain. Fixed effects are: Gender, hemisphere, age and region. Persons ID is a random effect. At start I've tried ...
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I have run a lognormal GLMM using the glmmTMB package, and I could use some help understanding the dispersion parameter. It is very large (2210), but there are no model convergence issues and no ...
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I have a question that relates to the use of IAT scores across timepoints. As part of a large health-based intervention my colleagues and I have obtained IAT scores at different timepoints, from which ...
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I'm analysing some occupancy data over a long time period, where there is a year that an invasive species was introduced, and I expect it to affect the trend for my organisms' presence. I would like ...
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I am running a GLMM on my small dataset ($n=31$) in a repeated measures study that has $2$ groups and $5$ conditions (conditions are fixed for everyone). I am interested in main effects of group and ...
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I'm currently working on a logistic regression, but I'm unsure whether I should log-transform my predictors. Here is the formula for my model: ...
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I'm analyzing an experiment I ran with bumblebees, and really struggling with choosing the appropriate model. In the experiment, each bee made feeder choices across two temperature conditions: ...
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I could really use some help interpreting my GLMM and its diagnostics. This is my first time posting so let me know if you would like further clarification on anything! I'm trying to build a model for ...
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I am fitting models with MASS::glmmPQL of the form ...
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Model Details After some thought about previous questions I've posed here, I've decided to add splines to non-linear terms in my logistic GLMM. However, there are a number of questions I have about ...
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I am investigating the properties of a GLMM with Beta response distribution and a hurdle component to model the probability of observing a zero. I have fit a frequentist version of the model using the ...
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I am building a glmm where I have 3 PCs (principal components) as predictors in the model. These PCs are based on soil variables (pH, CEC, nutrients) so I have reason to believe they may interact with ...
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I am trying to understand differences in the time spent on two sides of a Y-maze between treatments. The response variable is the proportion of time spent on one side of the maze [0,1] (see below). I ...
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I am using DHARMa to check dispersion in my glmm Poisson model and have uncovered significant UNDER-dispersion. Is this something I need to address ? I know significant overdispersion can cause issues ...
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I'd like to create a graph for my paper that visualizes my binomial glmm, ideally with confidence intervals. However, my CIs using ggpredict came out a bit funky. Here is some example code using the ...
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