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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Do I need more than one random slope?

When constructing a GLMM in R, do I need more than one random slope if I "see" that slopes differ for multiple continuous variables? In my case, I am analysing the number of plant species (...
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Elastic net package for mixed effects models?

I know about glmmLasso but would prefer to use elastic net. I wonder if there are any glmm analogues of glmnet out there, or if ...
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257 views

Should random effects be included in fitted values when making a binned residual plot for a binomial GLMM?

The binned residual plot in the R arm package is often recommended as a way to check if a logistic model is making any systematic errors. The general idea is that the mean residual for a group of ...
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Logistic Regression with dependent observations

I have a dataset that contains 100 different patients over 5 year’s period. Every patient is examined each month with regard to particular illness and marked as healthy or ill (0 or 1). Every person ...
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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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176 views

What to do if your regression residuals aren't normally distributed, cannot be transformed and do not conform even when outliers are removed?

I ran a regression on R and my shapiro wilk test showed that some of my residuals are not normally dsitributed. I cannot transform the data to fit a normal distribution and even when i remove outliers,...
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Mixed-effect logistic regression with very large dataset

I am conducting linguistic research to determine whether a property of the subject (animacy) of a sentence has its effect on whether a particular kind of preposition phrase will be mentioned. I ...
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Don't understand why glmm random effect variance is zero. Have reviewed similar questions still dont get it

I study a colonially-nesting bird species. I am trying to perform an AICc evaluation of GLMMs for a nest site selection study. I collected data at nest sites and paired random sites. I want to ...
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273 views

GLMM for count data using square root link in lme4

I have data from a field survey. The objective of the study is to relate number of seedling (respond variable, count data), landform (exploratory variable, categorical variable with 3 levels) and ...
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128 views

To choose between linear or generalised mixed effects model, what is the most important thing to consider?

Linear mixed effects models are for continuous variables. Generalised ones are for non continuous, e.g., binomial. We have a task in which subjects can get each item correct or incorrect. I'd say ...
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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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Interactions between random effects

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 ...
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Help with zero-inflated generalized linear mixed models with random factor in R

My study has a complicated design and I am not sure if I am modeling my zero-inflated data correctly. I have seed abundances and seedling abundances for 11 species. I have one main "treatment" with ...
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248 views

GLMM, introducing weight variable changes Pseudo-R^2 but not AIC

Here is a reproducible example using R, where I noticed that adding a uniform weight value to a glmm,the AIC of models stay the same but Pseudo R^2 gets reduced a lot. Why? ...
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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 ...
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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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How to handle underdispersion in GLMM (binomial outcome variable)

I'm working on the following model in R: ...
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How to set up an intercept-only mixed logistic regression in order to test for difference from 50% chance level?

In my experiment, subjects repeatedly had to make a binary choice between A and B, and I want to test if subjects (as a group) differed from 50% chance in preferring A over B. Is there a way to test ...
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GLMM - between, within and nested

I'm not entirely sure of fitting the model for experiment we've made. The variables and relevant description are as follows: ID - participant ID Trial - 60 for each participant Memory - between ...
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166 views

BIC in Item Response Theory Models: Using log(N) vs log(N*I) as a weight

In IRT software packages and in the literature it is common to calculate the BIC as $$ \mathrm{BIC} = -2 \cdot \mathrm{logLik} + \log(N)\mathrm{Npars} $$ where $N$ is the number of rows in wide ...
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774 views

Cumulative counts or counts for Poisson regression

I have a set of data with measurements X1 and X2 across multiple time points, T1, T2 and T3. I would like to conduct a Poisson regression using X1 and X2 on the counts of a phenomenon. An example of ...
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Diagnostic plots for lmer

I am trying to produce a glmm using the lme4 package in R. To validate my model I would like to produce some diagnostic plots ...
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899 views

Does the VIF make sense for a model with categorical variables?

I'm trying to detect multicollinearity in my model, it has count response variable and some proportional and one categorical explanatory variable called site. In R the model looks like this: ...
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GLMM output interpretation (correct text)

I used the lmer function in the lme4 package in order to assess the effects of 2 categorical fixed effects (1º Animal Group: ...
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Time series models (e.g. ARMA) a type or extension of GLM? Particular/stipulated forms of dependence in time series models

I am trying to understand the relationship between ARMA Time Series models and the GLM (Generalized Linear Model) family of models. As far I know, all GLMs have the following 3 components: 1) random ...
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628 views

Interpreting Random Effects for Poisson GLMM

There seem to be a few answers for normally distributed models, but after some searching I could only come across this page for Poisson mixed models. I want to be certain I am interpreting the random ...
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data visualization following glmm in lmer

Everything I know about glmms is from the internet, and after extensive searching, I haven't come across a good clearcut guide for how to visualize your data in a way that is relevant to hypotheses ...
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555 views

Is this longitudinal data too complicated for GLMM or GEE?

After writing this post, I've realized that I am running around in circles, chasing my tail. Any help approaching this problem would be greatly appreciated, as I think I just need to bounce ideas ...
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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 ...
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Correct estimation of arguments for glmmLasso function

I am using glmmLasso for variable selection. In my case, n is slightly less than p and ...
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Variance explained by random effects using lme4

I am using the glmer() function from the lme4 package to run a GLMM using the poisson distribution. In all the examples that I see, the random effects part of the ...
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If using Glmm with Gamma distribution do i need to transform my data to be between 0 and 1?

When creating a Glmm with Gamma distribution do I need to transform my response variable data to be between 0 and 1?
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How to do binary logistic regression on people (couples) clustered within homes?

I am looking at the relationship between housing characteristics and a health outcome. To make the example simple, I have data for a continuous predictor (exposure) collected from 1000 homes and ...
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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 ...
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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 ...
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Level-2 predictions with lme4/glmer model

Let's say I've fitted a 2 level model with glmer like this: ...
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8k 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 ...
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1answer
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Can you use glmmTMB to simultaneously model offsets and zero-inflation?

I'm currently modelling microbial data, with multiple samples and groups of samples. Two problems arise with my data: 1) The data is zero-inflated and dispersed (large variation); 2) Each sample has a ...
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2answers
456 views

Binomial vs. quasi-binomial model

I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different ...
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216 views

How many groups are needed to reliably estimate variance parameters of random effects in a GLMM?

I am looking at a panel data with binary outcomes in each year. The ultimate use of the model I build is for prediction. The cross-sections are quite tall (~100,000 non-cases and ~5,000-20,000 cases) ...
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1answer
710 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 ...
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Should I give more weight to goodness of fit or to conceptual approach?. Example

I am running mixed effects models with percentage data. I run my model using a gaussian distribution approach. AIC=-258, my conditional and marginal pseudo-R squares were 0.33 and 0.11 respectively (...
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Poisson glm for rank or score data?

I have a question about analyzing a dataset that I'm currently working with. Each row of the dataset represents an individual songbird, and its reproductive success over the course of a breeding ...
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1answer
307 views

What does it mean when a low number of quadrature points gives a very different GLMM fit?

I am interested in a logistic regression model with 10 fixed-effects parameters and random intercepts, which I can fit using the lme4::glmer function in R. The ...
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1answer
691 views

Understanding binomial GLMM with three fixed factors

I am analyzing my data using a generalized linear mixed model in R. My design has three categorical variables: proficiency (three levels: Chinese vs English vs ...
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2answers
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Standardize non-normal predictors before performing binomial GLMM using mean and sd?

I am planning to predict a binomial variable (1/0, a used point by an animal or point available to an animal in its range) using several continuous, distance-based predictor variables (distance to ...
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71 views

Use Poisson or Linear Regression for Longitudinal Data?

I want to fit a regression with repeated measurements Outcome:number of cigarettes smoked in the jth month (j=1,2,3) The outcome variable DOES NOT follow a normal distribution. The outcome values ...
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1answer
866 views

Calculating confidence intervals of marginal means in linear mixed models

I'm using different R packages (effects, ggeffects, emmeans, ...
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1answer
310 views

Assessing the fit of GLMM implementation of a Rasch model to binary data using lme4

I'd like to assess the fit of the kinds of models described by de Boeck et al (2011) (http://www.jstatsoft.org/v39/i12). They are GLMM implementations of Rasch family models, e.g.: ...
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How to analyse spatial data where the depending variable is binary

I have to test which factors influence game damage in fields. I mapped areas with damage and those without. It was not always possible to map 100% of a field, so there are also areas where it is ...

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