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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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Let's say we have a model ...
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Consider a statistical problem where you have a response variable that you want to describe conditional on an explanatory ...
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I am trying to understand when to use a random effect and when it is unnecessary. Ive been told a rule of thumb is if you have 4 or more groups/individuals which I do (15 individual moose). Some of ...
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I am wondering what the differences are between mixed and unmixed GLMs. For instance, in SPSS the drop down menu allows users to fit either: ...
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I am currently struggling with finding the right model for difficult count data (dependent variable). I have tried various different models (mixed effects models are necessary for my kind of data) ...
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We’ve run a mixed effects logistic regression using the following syntax; ...
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I have seen questions about this on this forum, and I have also asked it myself in a previous post but I still haven't been able to solve my problem. Therefore I am trying again, formulating the ...
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I'm hoping somebody can help with what I think is a relatively simple question, and I think I know the answer but without confirmation it has become something I just can't be certain of. I have some ...
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I have a repeated-measures experiment where the dependent variable is a percentage, and I have multiple factors as independent variables. I'd like to use glmer from ...
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I have a GLMM with a binomial distribution and a logit link function and I have the feeling that an important aspect of the data is not well represented in the model. To test this, I would like to ...
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I am trying to use lme4::glmer() to fit a binomial generalized mixed model (GLMM) with dependent variable that is not binary, but a continuous variable between zero ...
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While learning about the Generalized Linear Mixed Models, I often see the above terms. Sometimes it seems to me these are separate methods of estimation of (fixed? random? both?) effects, but when I ...
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I am using the glmer function from the lme4 package in R, and I'm using the bobyqa optimizer ...
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Sorry if I'm missing something very obvious here but I am new to mixed effect modelling. I am trying to model a binomial presence/absence response as a function of percentages of habitat within the ...
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I have a data set in which the variable I'd like to use as a random effect only has a single observation for some levels. Based on the answers to previous questions, I've gathered that, in principle, ...
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I am modelling invertebrate.biomass ~ habitat.type * calendar.day + habitat.type * calendar.day ^ 2, with a random intercept of ...
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In short, my question is as follows: Why is it common to assume normally distributed random effects (especially in generalized linear mixed models)? A longer version: Under some circumstances, an ...
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I'm running a logistic regression model in the form: lmer(response~1+(1|site), family=binomial, REML = FALSE) Normally I would calculate the ICC from the ...
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In searching for any info about marginal model and random-effects model, and how to choose between them, I have found some info but it was more-or-less mathematical abstract explanation (like for ...
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The short version: 1)Are there any published critiques of the use of $R^2$ for GLMMs, in particular the popular approach of Nakagawa & Schielzeth (2013) A general and simple method for obtaining $...
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I'm trying to analyse some data from a set of bird surveys. My response variable is "bird abundance", which is the number of birds counted over a five-minute period. These five-minute counts were ...
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I struggle with understanding the dispersion model and dispersion parameter of glmmTMB , and could not find answers anywhere. I constructed a GLMM using ...
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I have recently been introduced to to Hommel Hochberg corrections. I am trying to find a simple explanation about what this actually is/does, but am having no luck. Can anyone please give a brief and ...
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I have data with an outcome of 0 or 1 (binary) representing success or failure. I also have two comparison groups (Treatment vs. Control). Each subject in the study contributed 2 observations (the ...
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I have very recently started learning about Generalised Linear Mixed Models and was using R to explore what difference it makes to treat group membership as either fixed or random effect. In ...
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I have a GLMM of the form: lmer(present? ~ factor1 + factor2 + continuous + factor1*continuous + (1 | factor3), family=binomial) When I use <...
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I have the following model: > model1<-lmer(aph.remain~sMFS1+sAG1+sSHDI1+sbare+season+crop +(1|landscape),family=poisson) ...and this is the summary ...
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I have run into a number of practical questions when modeling count data from experimental research using a within-subject experiment. I briefly describe the experiment, data, and what I have done so ...
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This question/topic came up in a discussion with a colleague and I was looking for some opinions on this: I am modeling some data using a random effects logistic regression, more precisely a random ...
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In the output of a GLMM, using a binary variable as response variable and continuous variables as explanatory variables [family = binomial(link="logit")], I obtain, for each variable, an estimate ...
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I am modeling count data (with offset / exposure parameter). My modeling strategy is use of a Poisson model and a negative binomial regression model. I compare model AICs, which are about -760 for my ...
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First let's simulate some data for a logistic regression with fixed and random parts: ...
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I have a random intercept logistic regression (due to repeated measurements) and I would like to do some diagnostics, specifically concerning outliers and influential observations. I looked at ...
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Should I include random effects in a model even if they aren't statistically significant? I have a repeated measures experimental design, in which each individual experiences three different ...
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I have always heard random effects require a minimum number of levels to be correctly specified in a hierarchical (mixed-effects) model. I can admit to following this rule without question (mostly ...
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I have some questions regarding specification and interpretation of GLMMs. 3 questions are definitely statistical and 2 are more specifically about R. I am posting here because ultimately I think the ...
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Intro I have participants who are repeatedly touching contaminated surfaces with E. coli in two conditions (A=wearing gloves, B=no gloves). I want to know if there's a difference between the amount ...
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It's my first question here, I hope I'll ask it correctly. I am trying to find out how to analyse non-integer, count data (yes!). I am looking at the effect of a given treatment on habitat suitability ...
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What I want to do is to construct GLMM's to evaluate resource selection, and I have a set of variables (some representing distances and others representing % of land cover). Can I test for ...
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I read about generalized estimating equations (GEE) here, here and at other sites. It is mentioned in first of above links that "the parameter estimates are nearly identical" for linear ...
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I'm having a hard time getting on the same page as my supervisor when it comes to validating my model. I have analyzed the residues (observed against the fitted values) and I used this as an argument ...
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My design is as follows. $y$ is Bernoulli response $x_1$ is a continuous variable $x_2$ is a categorical (factor) variable with two levels The experiment is completely within subjects. That is, each ...
Bill Simpson's user avatar
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I have long-term collection data, and I'd like to test, whether the number of animals collected is influenced by weather effects. My model looks like below: ...
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I'm considering a mixed-effects model to try to understand factors that influence the number of ticks sampled on wild rodents. My data is nested so that I have one tick count per rodent, multiple ...
Claire's user avatar
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I want to estimate a multilevel multinomial logit model but I am struggling with the terminology and notation used by the R-package MCMCglmm. There is documentation ...
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I would like some clarification whether my model is well specified or not (since I do not have much experience with Beta regression models). My variable is the percentual of dirth area in the denture....
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I am using glmmLasso for variable selection. In my case, n is slightly less than p and ...
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I want to know how to use Poisson GLMMs when we have unequal samples available for different groups/clusters/participants in data. Imagine a study where each of the 60 participants are given 1000 ...
medium-dimensional's user avatar
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So, the beauty of the $R^2$ in linear models or the deviance-based pseudo-$R^2$ from GLMs is their intuitive interpretation for non-specialists. There's also some nice developments on this front for ...
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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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