Questions tagged [glmm]
Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
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logistic regression model with mixed effects?
I have a dataset where the primary result is the test results (a binary variable 0 or 1) from doctors. I want to see if a continuous variable X is going to affect the test result.
I think in this ...
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Do I need to find odd ratio for the result of binomial GLMM?
I have run a binomial generalised linear mixed model (GLMM) via the lme4 package in R. Then I have got the result for it. In the paper, I wrote this for the result: b = 2.23, SE = 0.59, p < 0.01.
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Question on slopes in mixed models: two random slopes for one factor?
I was wondering if I'm missing something as I have never seen one factor x as part of two slopes in a (generalized linear mixed/) random effects model like this: y ~ (x | id) + (x | item). Can that be ...
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Dispersion parameter in DHARMA
I have a question about the interpretation of residual diagnostics using DHARMa.
I fitted a binomial mixed model and used DHARMa for model diagnostics.
...
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GLMM with crossed random effects
We're interested in whether a lipreading training improves audiovisual speech perception. Participants that either received lipreading training or not completed a speech comprehension task at two time ...
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Heavily unbalanced sampling design - Structure of GLMM (ecology)
I am currently working on a dataset (count data) from a rather heavily unbalanced sampling design.
In particular, I would like to be able to predict the abundance of the studied species according to ...
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How to deal with collinearity or concurvity between fixed effect variables and random effect variables?
My current work involves asking subjects from different groups to pronounce multiple words, and I am interested in understanding the relationship between word duration and groups.
In R syntax, this ...
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Zigzag residual plot
What is not right with a model that produces this kind of residual plot? Does it have to be discarded?
My data is egg production (counts, but cumulative) over a period of 55 days for 7 treatments+...
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cross-validation to find optimal Lambda in glmmLasso function
I am currently applying the code provided in the demo of the glmmLasso package to my data.
However, I stumbled over the part where the sample is split into 5 folds.
It seems like in the provided code ...
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Rule of thumb (10obs:1fixed effect) to avoid overfitting GLMMs
This question has been asked elsewhere (e.g., here) but has not yet been adequately answered.
I have a nested dataset of annual bird counts (n=5 response variables) across 12 years taken from 31 ...
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Stuck on interpretation/validation of GLMM results
I am using glmmTMB in R to identify which weather variables (n=5) most influence annual bird counts (n=5 responses) from different monitoring sites. So far, I have ...
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Inclusion of candidates in model averaging with GLMMs
I am using GLMMs to examine the influence of 5 weather variables on different biological count variables in R. With n=5 weather variables and modelling main effects only, I have 32 candidate models (...
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Is it necessary to standardize predictors when model averaging without interactions?
I am using GLMMs to model the influence of weather variables on bird counts, and using model averaging to generate parameter estimates for each predictor. Standardizing (centralizing) predictors is ...
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Generalized linear mixed model for assessing diagnostic accuracy of doctors
I am trying to determine how a doctor's expertise (either expert:1 or not an expert:0) affects their diagnostic accuracy of medical images. Additionally, I'd like to examine how the hospital at which ...
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GLMM to identify associations between longitudinal predictor variables and binomial outcome
I am trying to perform a generalized linear mixed-effects model to understand if exposure to pollutants in specific time-points is associated with clinical severity of a given disease. All to be done ...
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How to use GLMM on factorial design, and resources for learning
If I have a 2^3 factorial design (2 levels, 3 factors) with 8 treatments that cover all 7 combinations + control for main effects and interaction effects, how do I model my data?
I want to use a GLMM. ...
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How do I use the glmer function properly with my data in R
I have a set of around 23k rows of data. It is a set of animal movement lengths (dist), going from 0 to several thousand, with the majority being around 50 to 100. The data doesn't have a normal ...
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Do I have to transform a heavily skewed numeric predictor into a categorical predictor in a binomial mixed model?
I am working on a mixed model with a categorical response variable and several categorical and numeric predictors. One of my numeric predictors is heavily skewed. Should I transform it into a ...
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Justification for using zero-inflated model in GLMM
I am using GLMMs in R to examine the influence of various continuous predictor variables (x) on several biological counts variables (y). My response variables (n=5) each have a high number of zeros (...
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how to interpret estimates from Beta GLMM with dummy explanatory variables
I have results of an experiment where each person had to estimate a share of certain types of city dwellers in two cities (A and V), and participants were assigned into one of two treatments (FIN or ...
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How to manually set the dispersion parameters for truncated negative binomial distribution in glmmTMB?
I am using glmmTMB to run a glmm with a zero-truncated error distribution. I would like to manually set the dispersion parameter in my model and found a webpage that suggests that this is possible. ...
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Hypothesis testing for random effect term in linear mixed model
In linear mixed model, we have
$$
y = X\alpha + Z u + \epsilon;
$$
which can be written in Multi-variate normal form:
$$
y \sim MVN(X\alpha, \tau_1V_1 + \tau_2V_2 + ... + \sigma^2 I) \\
\text{where } ...
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GLMM: glmer error when setting interaction terms
I am having difficulty fitting a generalized linear mixed-effects model in R.
I did a simple study looking at the variables affecting the triggering of camera traps. My response variable is binary (...
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fixed effects are not in line with random effects in a GLMM
I have problems wrapping my head around some findings: I found a significant (negative) interaction between the random interactions A:B and A:C. However, one of these fixed effects is 0 on the fixed ...
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setting up random factor in lmer4 [duplicate]
I have some data I want to analyze using the glmer function of the lme4.
participants watched 5 movies in random order (...
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How to manage a GLMM rank deficient
Some background into my study, I'm looking at the abundance of birds across sites in 5 different vegetation states, using a GLMM. The birds belong to 5 different functional groups and I'd also like ...
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Linear mixed effect model with years as random effect
I have to set up an LMM with just one independent variable and there is both monthly and yearly variation. I aim to get fixed effects coefficients (slope and intercept) for each month and year given ...
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Random and fixed effects for count data when repeated measure subject is point of interest
I'm having trouble understanding how I build a model to help me find out whether there are differences between referees and the personal penalties (temporary suspensions from the game) they give out ...
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Converting lme4 syntax in lmm equation
I have computed a model with the lme4 package with the following syntax:
y ~ time*treatment*covariate + (1|subject)
The ...
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Correct way to visualize glmm with interactions in R?
I just want to make sure I'm visualizing mixed model with interaction terms the correct way. I fitted the following model with glmmTMB package in R.
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Should all the levels of a fixed factor be present in all the experimental trials?
I would like to analyze the output of 11 field trials dealing with the disturbance to soil after logging operations. The goal is to check if there are significant differences between two machinery: ...
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Appropriate family to use for continuous response variable (proportion data) with both negative and positive values in glmm?
I have a continuous response variable from proportion data with both negative and positive values. Values in response variable range from 0.92 to -0.92. I would like to use glmms but not sure what ...
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How to pick a model based on AIC and number of factors?
I'm trying to create a linear mixed model for some of my data. I'm brute forcing a backwards step selection by taking out the least significant parameter both times. I went back 6 fixed effects and ...
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Transforming non-normal data to be normal in R
I have gone through a variety of posts and after natural log, sqrt, log10, and inverse transformations, one of my columns in R is not even close to being normal. I want to run a linear mixed model on ...
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Regression and Other Stories vs Data Analysis Using Regression and Multilevel/Hierarchical Models
Andrew Gelman and Jennifer Hill wrote Data Analysis Using Regression and Multilevel/Hierarchical Models back in 2006. Recently in 2020, they published Regression and Other Stories. Is anyone familiar ...
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glmmTMB - Model convergence problem; non-positive-definite Hessian matrix
I am trying to conduct a model with the glmmTMB package - depression as an outcome and stress as a predictor, including age, gender, working hours, and observation number (=time) as covariates. I also ...
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GLMM random effects (lmerTest) & dredge (MuMin) warnings
I am running a GLMM to determine which covariates may model the occurrence of a deep dive on a given day (binary value of 1 where 'depth>500'). I have 24 individuals with daily data over 1 to 6 ...
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Mixed model for non-normal continous data
I am dealing with longitudinal data (Visual analogue scale (0-10)), and my data is skewed and contained several zeros. I have missing data (MAR) and can't use ANOVA with the non-parametric test, so I ...
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Analysing longitudinal data with unequal survey frequency
I am analysing a special kind of longitudinal data set collected via ecological momentary assessments.
Participants behaviour/state was observed for multiple days, which resulted in one data point per ...
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Singularity in Poisson GLMM - when is it better to switch to GLM?
I am analysing count data (count of observations per day of certain mammal species) at 12 different sites. My dataset consists of ~120 days of observations at each of the different sites. For each ...
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Minor over-dispersion: reasonable to proceed with standard poisson GLMM?
I am using a Poisson GLMM with glmer() from lme4 package in R. My data is ecological count data, and the model has one random ...
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Analyzing/fixing non-normal continuous data using glmmTMB, Gaussian distribution, having significant deviations in residuals?
I am trying to analyze continuous data (individual mass) using a GLMM. I have deduced based on previous work and recommendations to use a Gaussian distribution. However, when I look at my residuals I ...
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When to use GEE vs. GLMM
I'm trying to decide whether I should use GLMM or GEE for my analysis.
My study is repeat cross-sectional using longitudinal data (with 3 timepoints). I have a binary outcome and both continuous and ...
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Should I transform my count response variable to continuous response variable for mixed models (glmms)?
I conducted an experiment where I counted the number of infected leaves per plant. There were four replicate plants per treatment (total two treatments). So my response variable is positive count data,...
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R GLM get probability density for a GLM model
I train a GLM via
model<- glm(formula=y \sim x_1+x_2,family=Gamma
(link=log),data=...)
I now would like to get the probability density function of $f_{\theta(...
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Power analysis for Generalized Linear Model with gamma family and log link in R
I have a data set with 360 observations. I run a glm with a gamma family, and I want to show that I fail to reject the null hypothesis. Although the p-value greater than 0.05 is kind of showing that ...
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Can I test the difference between subjects in a GLMM?
I have the following GLMM model (using R with the lme4 package):
resp ~ condition * level + (1 | subject)
I have a clear difference between levels and I would like ...
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GLMM for categorical predictor and response variables, with multiple levels
I want to test in R if a factor variable with five levels predicts the values of another factor variable that has the same number of levels.
In each variable, I have counted abundance in a different ...
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Specific configuration of a logistic multilevel regression (GLMM): Polarization hypothesis
I'm looking forward generating a generalized linear mixed model for the question whether there is a hint that the development of the broadband network (as proxy for the grade of automation in a ...
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Is it appropriate to use GLMM for the dataset I have
I am interested in determining the influence of biotic (initial height) and abiotic (light, canopy, soil moisture content, soil nutrients, rainfall, and temperature) factors in the absolute height-...
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