Questions tagged [glmm]

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

Filter by
Sorted by
Tagged with
19
votes
1answer
5k views

How do you deal with “nested” variables in a regression model?

Consider a statistical problem where you have a response variable that you want to describe conditional on an explanatory ...
35
votes
3answers
54k views

Difference between generalized linear models & generalized linear mixed models

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: ...
32
votes
2answers
64k views

Dealing with singular fit in mixed models

Let's say we have a model ...
28
votes
2answers
25k views

Why do I get zero variance of a random effect in my mixed model, despite some variation in the data?

We’ve run a mixed effects logistic regression using the following syntax; ...
22
votes
2answers
21k views

How to apply binomial GLMM (glmer) to percentages rather than yes-no counts?

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 ...
19
votes
1answer
11k views

How to fit a mixed model with response variable between 0 and 1?

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 ...
13
votes
1answer
25k views

Fitting a binomial GLMM (glmer) to a response variable that is a proportion or fraction

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 ...
30
votes
2answers
24k views

Diagnostics for generalized linear (mixed) models (specifically residuals)

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) ...
6
votes
2answers
5k views

Conditional vs. Marginal models

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 ...
2
votes
1answer
114 views

Both variables of my GLMM output are significant. Don't know how to interpret it?

This is more of an interpretation question than anything. I have run a GLMM with two fixed factors (both of which have two levels) and two random factors. The outputs from the model are as such: <...
19
votes
2answers
11k views

Random effect equal to 0 in generalized linear mixed model [duplicate]

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 ...
22
votes
5answers
21k views

How to assess the fit of a binomial GLMM fitted with lme4 (> 1.0)?

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 ...
18
votes
2answers
4k views

How will random effects with only 1 observation affect a generalized linear mixed model?

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, ...
4
votes
1answer
3k views

Should quantitative predictors be transformed to be normally distributed?

I am always struggling with normality testing for quantitative predictors (no factors) and transforming them to normality. If I am running a GLMM and my predictors are really non-normal, should I ...
41
votes
2answers
57k views

How can I test whether a random effect is significant?

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 ...
8
votes
1answer
3k views

Resolving heteroscedasticity in Poisson GLMM

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: ...
1
vote
1answer
3k views

Accounting for time in repeated measures glmm, R

I have some count data of advanced stage juvenile snails in tanks that are sampled every 4 days for 4 sample points. I want to see how much the snail development stages change with a changing dosage ...
1
vote
1answer
697 views

Computation and interpretation of marginal effects in a GLMM

I am currently working on a GLMM model which uses a Poisson distribution and need to compute and interpret marginal effects from this model. The model outcome consists of a count (COUNT) collected ...
13
votes
1answer
13k views

Marginal model versus random-effects model – how to choose between them? An advice for a layman

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 ...
16
votes
2answers
49k views

r glmer warnings: model fails to converge & model is nearly unidentifiable

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 ...
13
votes
1answer
12k views

Calculating ICC for random-effects logistic regression

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 ...
8
votes
1answer
9k views

Model selection: can I compare the AIC from models of count data between linear and poisson models?

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 ...
3
votes
1answer
543 views

Which random effects to include in this GLMM?

In my study growth of plants was measured in different years on different plots (all plants were measured in all years). The question I'd like to answer with my model is: Which factors influence ...
3
votes
1answer
815 views

Logit link in GLM and inverse logit

I am calculating a generalized linear mixed model (GLM) with a two-column (n successes/failures) binomial response using the the lme4 package in ...
0
votes
1answer
140 views

Prediction using fixed effects in glmm

I have the following generalized linear mixed effects model (mcmcglmm in R) with data based on this paper. Sex is a two level factor (M or F), ...
10
votes
3answers
23k views

Fixed vs Random Effects

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 ...
11
votes
1answer
9k views

What are Hommel Hochberg corrections?

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 ...
12
votes
1answer
806 views

How are PQL, REML, ML, Laplace, Gauss-Hermite related to each other?

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 ...
6
votes
0answers
1k views

When and why do I have to use “trait” for multinomial multilevel models with MCMCglmm in R?

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 ...
5
votes
1answer
9k views

Conditional logistic regression vs GLMM in R

I have paired data (GWAS case/control study) and I have heard using conditional logistic regression or generalized linear mixed models (GLMM) is appropriate. Which should I use in this case? Why would ...
3
votes
2answers
3k views

Modeling reaction time with glmer

According to Lo and Andrews, 2015 (https://doi.org/10.3389/fpsyg.2015.01171) raw Reaction Time (RT) should be analyzed with a GLMM, instead of transformed values with LMM or even ANOVA. They and ...
3
votes
1answer
2k views

Negative binomial vs binomial distribution for proportion data

I'm sorry if this is a duplicate question; I searched around for an answer for some time, but couldn't find anything. I want to build a model in R, with the proportional number of individuals ('count'...
5
votes
1answer
1k views

Adding an observation level random term messes up residuals vs fitted plot. Why?

I run a mixed effects generalized model for proportional data (response variable). I used binomial family and logit link function. I suffered from overdispersion so I added an observation level random ...
3
votes
1answer
8k views

Order of nested random effects in lme4

I just fit a model in lme4, and I'm wondering what the heck I fit... I have individuals id, and each is measured pass/fail on items that can be described using two ...
1
vote
1answer
944 views

GLMM multilevel (hierarchical) model

I want to study the classroom and the school effect/result on the pupil's success (or not) at school. I also want to know the effect of the age, the gender (of the pupils) and if the pupils have or ...
8
votes
2answers
5k views

Effect size in GLMM

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 ...
3
votes
1answer
294 views

Time Series of Wound Healing percentages / proportions

I am looking at wound healing over time and have hit a wall with analysis. My data consist of injury cases which are tracked over time. Injury healing is measured as percentage wound area/perimeter ...
2
votes
1answer
4k views

Testing whether random effects are normally distributed in R

I've been working on a GLMM in R and I see that an assumption of the test is that the random factor must be normally distributed (that is, unless you're using a package like ...
1
vote
0answers
5k views

Generalized Linear Mixed Model in R with repeated measures

I am trying to investigate how four variables (var1=continuous, var2=factor, var3=factor, var4=continuous) influence the number of trials individuals approached (out of total nr of trials --> binomial)...
1
vote
1answer
870 views

Zero-truncated Poisson model

In the theory of generalised linear models, you may use the exponential family to find the mean and variance of certain distributions. How would the mean and expectation of the zero-truncated Poisson ...
1
vote
0answers
250 views

How to interpret the results from random coefficient Poisson data analysis

I have fitted random coefficient Poisson analysis in R. I have obtained the following results: ...
4
votes
1answer
2k views

Crossed fixed effects model specification including nesting and repeated measures using glmm in R

Background: I am interested in looking at the effects that Culture, Treatment and Time have ...
10
votes
3answers
14k views

Generalized linear mixed models: model selection

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 ...
9
votes
1answer
10k views

Help interpreting count data GLMM using lme4 glmer and glmer.nb - Negative binomial versus Poisson

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 ...
4
votes
3answers
4k views

What are the assumptions of a Gamma GLM or GLMM for hypothesis testing?

What are the assumptions when doing hypothesis testing using a Gamma GLM or GLMM? Are the residuals suppose to be normally distributed and is heteroscedasticity a concern like the Gaussian (normal) ...
4
votes
1answer
6k views

Nested random effects in lme4 R

Background: I have data on time to infection across multiple sites across a gradient. The design involves 2 latitudes (In and Out) with sites 1 and 2 nested within “In” and sites 3 and 4 nested within ...
3
votes
1answer
3k views

In what sense is the interpretation of coefficients in a GLMM subject-specific?

There is something I'm not quite understanding conceptually about the output from generalized linear mixed models. I have read that the target of inference in GLMMs is subject-specific. For example, ...
2
votes
1answer
1k views

Intraclass correlation with count data

I want to calculate the ICC between 3 different measurements where the dependent variable is a count. As far as I understood, if the data were normally distributed, I would use a repeated measures ...
9
votes
3answers
312 views

Bacteria picked up on fingers after multiple surface contacts: non-normal data, repeated measures, crossed participants

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 ...
9
votes
1answer
3k views

Should I exclude random effects from a model if they are not statistically significant?

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 ...