# Tagged Questions

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

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### GLMEM vs LM on proportions within random effects

I'm doing a project involving outcomes measured for people clustered within departments. The departments had previously been randomized to a treatment or control condition. One of the main outcomes ...
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### GLMM for repeated longitudinal count data

I have multiple longitudinal data sets (3 repeat trials) of microbial count data for a cohort of animals (each trial had different animals); animals belonged to either a treated or untreated group and ...
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### linear mixed model with 3 group categorical response

I've been looking all around the webs but cant find a conclusive answer. I have count data for a longitudinal study where subjects were grouped into three treatment groups (A,B,C) and blocked by ...
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### When 2 variables are highly correlated can one be significant and the other not in a regression?

In regression, when 2 parameters are correlated and added to a model separately, how likely is it that one parameter will be a significant predictor of the response variable while the other is not? ...
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### Post Hoc Test of interaction factor in binomial glmm with proportions

I've performed a binomial glmm, because my data are proportions of a species in a sample of +/- 100 Individuals. I test the interaction of two factors and use car::Anova to get the p-values. My ...
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### Should I consider time as a fixed or random effect in GLMM?

I am attempting to determine if a type of pesticide is influencing the abundance of a particular species of bird. I have 35 years of data, which was collected along roadside survey routes that are run ...
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### What's the difference between a random intercept and a dummy variable?

I usually work in SAS or R, so when I code a GLM with a random intercept, it's usually pretty easy. However, I've been running into a few problems (too complicated to get into here) where it might be ...
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### Convergence of GLMM vs GEE

I am tring to figure out an issue which bothers me for quite some time, maybe you can help me understanding it. I wrote a SAS program to simulate the power (power analysis) for an experiment, where I ...
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### Expected significance level (alpha level) for a binomial glmm using glmer()

I have data on a investigation with only three real replicates. However I have repeated measurements, that probably improve the power of the analysis. Since my response is proportional data I use ...
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### GLIMMIX with two repeated measures variables

I'm new to GLMMs and want to check that I have used the appropriate syntax for my analyses. I'm analysing a longitudinal study looking at children's ('subject') behaviours ('beh') towards eleven ...
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### Generalized Linear (Mixed) Modelling for different scales

I am struggling conceptually with how I can best model my dataset. My data is the abundance of spiders collected from 20 permanent traps in a forested area. These traps have been repeatedly ...
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### How to interpret the overdispersion?

I fitted a generalized linear mixed model: a three-level beta-binomial logistic regression. The dependent variable is a proportion variable stemming from a limited number of trials (e.g. a value of ...
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### Appropriate random effects GLMM analysis for mean count data? R

I'm trying to find the right way of using a mixed effects approach on some mean count data (animal visits per day to a feeder) in R. I have two interacting fixed effects (both factors) and 2 random ...
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### Correct GLMM model for unreplicated factorial design

My experimental design is as follows: Three sites (Treatments) that were not replicated (Healthy Control [HC], Untreated [U], and Treated [T]). Six independent measurements (Samples) of a response ...
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### Is training error ever “good enough” when we have a large enough sample?

I recently had a (more experienced) coworker tell me that when you have a large enough sample, training error should be "good enough" to assess model performance. His point was that with a ...
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### Non zero average residuals on mixed effects GLM using lme4

I've ran an experiment with binary data and subject specific random effects using R's lme4 package: ...
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### Convergence error in r.squaredGLMM() but not glmer() fit

I am fitting binomial generalized linear mixed effects models with 2-8 fixed continuous variables and one random effect with 8 levels. The data set has about 700 points. I am using package lme4, ...
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### How to get resonable predictions of logistic models in MCMCglmm without removing the global intercept?

I am using the MCMCglmm package building mixed effect models on my data with a categorical response column (data: R1 column). My purposes are to predict the ...
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### GLMM - age and time correlated

I've been trying to fit a mixed model in R however since age and time are correlated (both increase) I'm having some problems figuring out the best option. I have data on 500 children, between 2005 ...
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### Categorical dependent variable - multilevel modelling using SPSS

is it alright to run a multilevel model using a binary categorical dependent variable (0,1). The model is running fine, but is there a better way to deal with it? Many thanks!
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### 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 ...
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### Errors in R for running GLMM's with a repeated measure, using AICc. Any alternatives?

I'm analysing the relationship that kites have with different land-use classes and climatic factors during the breeding and non-breeding seasons, and to do this, I'm running GLMMs with the response ...
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### Calculating effect & CI of continuous variable when class covariates are set to their mean

I'm trying to plot the effect of a continuous variable (VAR1) on the response from a Generalized Linear Mixed Model. In other words, I'd like to predict the response y when VAR1 is x, setting all ...
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### Should I use GLMM or GEE for analyzing data from a repeated behavioral economic game experiment (e.g., iterated prisonerâ€™s dilemma)?

I have data from a repeated behavioral economic game experiment in which dyads play the prisonerâ€™s dilemma game for 60 rounds. In each round, each player in a dyad decides to either cooperate (1) or ...
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### To categorize age explanatory variable or not

I fit gamma glmmPQL to repeated health costs of individuals. Age and claim number are continuous explanatory variables, the others are factors. Model does not converge generally when I define age into ...
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### Can AICc be used to select GLMM models with highly correlated predictors?

AICc is typically used for small sample sizes, because it avoids overfitting/selects fewer predictors. Highly correlated predictors are another cause of overfitting, so must I use AICc instead of ...
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### GLMMpql and GEE differences for univariate time series

I am hoping to compare a GLS, GLM, and GLM with autocorrelation for a non-normal data set using their RMSE values. I was originally intending to use a GLM-GEE, because I have seen them used in the ...
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### AICc penalising model complexity too highly?

My analysis uses a negative binomial GLMM with total revisits as the dependent variable, treatment (factor with 4 levels: 0ppb, 4.8ppb, 20ppb, 133ppb) and size as fixed effects and colony as a random ...
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### Nested fixed effects in a GLMM, hypothesis testing

I am attempting a GLMM with nested fixed effects. Most examples of nesting that I see deal with random effects, but my experimental design is hierarchical by nature and I am interested in making ...
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### Gamma hurdle model for continuous response

I am modelling invertebrate.biomass ~ habitat.type * calendar.day + habitat.type * calendar.day ^ 2, with a random intercept of transect.id (50 transects were repeated 5 times) My response is ...
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### Problems when adding a variance structure into a GLMM

I did a GLMM model with proportional data using the lme4 package. This model has three categorical independent variables: Age (2 levels) Sex (2 levels) Status (2 levels) "Year" is the random ...
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### 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 ...
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### Obtaining significance for mixed GLMMs on count and binary data

I'm new to the software R and am trying to compute statistics on data from experiments on the offspring of lizards from two different thermal treatments - looking specifically at differences in their ...
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### Methodological test for choosing 'worse' models that make 'better' (more realistic) predictions?

I've run 4 models (simple LM, quadratic model, GLMM, and GLMM with quadratic) to predict tree age (age) from tree diameter (D) for each of 42 species (SPEC). The diameter data has all been log ...
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### Classifying treatment levels as categorical or continuous

I am running a GLMM where one of the independent variables is treatment in terms of pesticide concentration, with four levels: 0ppb, 4.8ppb, 20ppb and 133ppb. I am unsure whether to class this ...
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### Zero-and-one inflated beta regression vs. binomial GLMM?

I appreciate some help with deciding whether I should (and how to) construct a zero-and-one-inflated beta regression model. I want to use R to test the hypothesis that there is a ...
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### Determining significance of coefficients in glmer

I simply want to know how to work out the significance between coefficients in a model that is not displayed in the summary(glmer) output. ...
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### constructing a GLMM in R

I am trying to build a model to find out whether different leaf species differ in their decay rates. I collected spectral measures of these leaves over time, and I now have comparisons between those ...
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### Do I have heterogenity in my GLMM? And if, how do I fix it?

I'm fitting a GLMM model with overdispersion and excess zeros (using R packagae glmmADMB)and I think I have heterogenity. Here is a plot with all my IV against residuals (alls IVs reflect count ...
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### How to interpret the output of Generalised Linear Mixed Model using glmer in R with a categorical fixed variable?

I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I ...
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### How can I find the variance components from a MCMCglmm output?

I have a dataset in which birds (n=55) were measured twice for five different behaviours. I am now trying to statistically test if individuals are reapeatable/consistent in their behaviours or not. I ...
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### GLMM test P-value and inference

I am analyzing results of my artificial predation experiment using GLMM (glmer in R) where response variable is count and predictor variable [a-categorical factor with 2 level;b-categorical factor ...
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### Problem fitting Poisson GLMM with observation level random effect

I simulated count data with an observation level random effect, then fit a Poisson-family GLMM using lme4. The random effects estimated show a strange pattern when ...
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### 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 ...
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### Does something justify using bglmer to correct the warnings I got with the glmer

I conducted a risk factor analysis for which I got some warning messages. Data: bf= if the piglet got the disease=1 if the piglet didn't=0 y= year (categorical data) SOW= random effect of the ...
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### Proper use of model inference (AIC) (Burnham and Anderson) - when to explore more models

I am starting an analysis, for which I have a binomial response variable (species relative abundance) and continuous predictors (habitat variables). I have done some data exploration, and there is ...
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### 3 groups, 3 measurement points, poisson distribution, baseline difference: GLMM or RM ANCOVA?

I have a small experimental data set. 3 group (n's = 12, 12, 16). T1 is baseline before intervention. T2 post-intervention, T3 follow-up (times between testing vary). The DV is count data that fits a ...
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### Analyzing specific factor combinations

Is there any way to isolate specific treatments and test those for differences within an overall multi-factor model? I'm running a multi-way ANOVA. For my particular model, I have factors with ...