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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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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’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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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 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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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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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 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 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 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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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 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'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 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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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'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 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 am using glmmTMB and keep getting this diagnosis on my model when I check it: ...
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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 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 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 ...
Emma Neigel's user avatar
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4 votes
1 answer
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I conducted a mate choice experiment where, in n=31 trials, individual males could choose between 2 females, one being kin the other non-kin. The binary outcome was 1=chosen or 0=not chosen for each ...
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So I've done two separate tests, a PCA and a GLMM, using the same groups of individuals. The experiments have to do with animal behavior, so I did preliminary recordings of how the animals interact ...
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4 votes
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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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I am doing meta analysis for a one-sample proportion where some of the studies have 0 events. My understanding of the statistical literature is that: Traditional meta analysis methods that require a ...
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I am trying to understand species presence (1/0) within protected areas in Africa using a set of predictor variables. My data is not nested and contains no hierarchies within it. My question pertains ...
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When testing fixed effects in linear mixed effect models, Luke 2017 shows the following: Traditional likelihood ratio test (LRT) and Wald tests can be anticonservative (inflated type 1 error rate) in ...
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I am using buildglmmTMB to build a model for several different taxa. I have 700 ish taxa and ~10 effects. I have a script that loops through all the taxa and ...
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2 votes
1 answer
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I'm currently analyzing a microbial sequencing dataset (>10 000 of species) of ~40 000 samples/observations. Out of these 1000's of species I want to select single species and see if one of the <...
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I have a glmm in R with 4 fixed effects, 4 interaction effects, 2 random effects, and a binomial response variable. I have two versions of this model, both with the same model strucure: ...
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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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Context : I’m working with a longitudinal cohort of patients followed for substance use disorders (n ≈ 3300 observations, 900 subjects). The outcome is the number of days of substance use over the ...
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I have run a threshold model using MCMCglmm (binary response variable) and obtained the proportion of variance explained by the random effects, but how do I do this for my fixed effect?
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I am analysing the effect of three predictive variables and one random effect on a response variable, using a dataset with 204 rows of data. See variable description below: Response: measures for how ...
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I'm wondering the best approach to test the significance of fixed effects in GLMM in R (with a logistic linking function; binary outcome, 2 fixed effects, 1 random effect). My high-level aim is to ...
ahach's user avatar
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6 votes
1 answer
195 views

Update: Based on the responses and comments, I don't think I should use the GLMM because my patient ID (random effect) perfectly predicts treatment outcomes. Defining treatment outcome as a predictor ...
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If I am interested in understanding the effect of an interaction between treatment and covariates on the variance of my response, as well as how they may affect the mean of my response, can I write a ...
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I’m using a GLMM for my sleep study because it handles missing data and different observation lengths well. My data is unbalanced since this was a field study, not a controlled lab setting. Shift ...
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1 vote
1 answer
123 views

I'm fitting a negative-binomial model for count data in a repeated measures setting ...
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Hi, I have run a beta GLMMTMB in which my response variable ranges between 0-1 so I have used beta distribution. In my model I've got 3 continuous (numeric) predictors and 3 random effects factors, ...
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