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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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spark/H2O/hadoop/scala/R differences - what are they

I cant seem to understand what H2o and spark are. I understand what R is - its a language and you can build models (i.e. logistic regression models) with it Hadoop - from my knowledge, its a bigdata ...
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1answer
13 views

Intraclass Correlation Coefficient with Bayesian ordered-logit GLMM (STAN)

I am fitting a Generalized Linear Mixed Model for an ordered outcome, in form of an ordered logit, with random intercept and slope. For this task, I am going Bayesian by handling STAN through the ...
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0answers
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glmer: distribution law for number of events / time

Sorry if this is a basic question but I can't find an answer that is clear enough, so I prefer to ask. I want to model a number of events (number of gaze) that depends on the behaviour that one ...
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2answers
109 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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1answer
26 views

How to interpret a linear mixed model formula

In this model: Biomass ~ Species + (1|Site / Species) Biomass and Species are continuous variables, and ...
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0answers
8 views

Best method for predicting a binary DV from multiple IVs with time series data in R or Python?

Let's say I have a dataset of 100.000 cases that contains 5 variables recorded over a period of time in long format, like this: ...
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1answer
91 views

Why are random effects assumed to follow a normal distribution in (G)LMMs?

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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1answer
21 views

How to deal with left skewed data and generalized linear models

I am trying to look at individual variation in Pielou's evenness of parasite communities. I have a study in which ~60 animals were sampled nine times (every two-three months for two years). Samples (...
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1answer
33 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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30 views

How can I describe mathematical equation of GLMM, rather than R language

How should I describe the below equation for someone who does not know R languages: glmer(A ~ as.factor(B) + (1|C)), where A is a response variable, B is a explanatory variable, and C is the random ...
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0answers
9 views

GLMM nested design

First time asking, really need some help to fit a model. I have a large dataset to investigate if the number of plant individuals (response variable) in a community is related to pollination mode (...
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0answers
104 views

Residual pattern for mixed models (tried lmer and glmer)

I have studied the effect of site, specific area and depth on amount of organisms on kelp blades. Each site had two different depth with three frames on each depth. From each site I have analysed a ...
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1answer
23 views

Calculate (backtransform) coefficients of a Gamma (type inverse) GLMM in R

I calculated a Generalized Linear Mixed Model for speech times (DV) with fixed effects congruency (2 levels) and stability (3 levels). I include random effects of type subject and length of word. The ...
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0answers
29 views

Power and Sample Size Estimation for Logistic Regression (Mixed) Models

I am working on designing a study based on some initial pilot data and would like to conduct a sample size estimation. I have previously used simr with linear mixed ...
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3answers
226 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 ...
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0answers
17 views

glmer repeated measures

My design is: y is a proportion: var name CS Xw1 is a 2-level condition variable, within subjects (all subjects participate in both conditions): var name Language Subjects also participate across ...
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1answer
41 views

Why do I get a massive drop in AIC/BIC when adding a main effect that isn't even significant?

I have no explanation for this. Note how "school" isn't significant, yet the model with only that main effect has a much lower AIC (and BIC) than the one with time, intervention, and intervention*time ...
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0answers
31 views

GLMM model validity

I have fitted a glmm model using glmmTMB and DHARMa packages in R. The model is pretty perfect: neither overdispersed/zero-inflated or spatially autocorrelated. And the qq plot of observed vs. ...
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0answers
43 views

Interpreting results of ordinal logistic regression - ordinal dependent variable with 5 levels and continuous predictor variable

Using SPSS, I have run a generalized linear mixed model for repeated measures longitudinal data on an ordinal target variable: duration of hallucinations with levels of 0= "N/A" (no hallucinations), 1=...
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0answers
7 views

Interpreting GLMM results of quadratic data

I am trying to find the optimal soil characteristics for an animal given a large spatial database of count data. I have fitted a GLMM which is pretty spot on: was able to account for overdispersion ...
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1answer
32 views

R glmer: distribution for strongly right skewed data

I have the following dataset with daily home range sizes (meter95, meter50) per individual: ...
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1answer
22 views

GLMM summary and multcomp differences

Thanks you for you time to read this question. I run a GLMM because I have measure repeated on 100 individuals. The explanatory variable it is a proporcion so I use binomial family. The final model ...
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0answers
20 views

GLMM for non-normal nested data (SPSS)

I have a dataset which is quite complicated to me. The data is nested; multiple measures within the same person. Therefore no ANOVA, but GLM/GEE. However, my independent variable is not normally ...
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25 views

Mixed Effects models with nested sampling design

I have collected data as the following protocol: In a given area (the French Alps in my case), I worked in two pastures (= 2 sites called A and B) in which I defined 3 sampled plots representing 3 ...
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0answers
7 views

Theory about LMM -design matrices for nested and crossed effects

I want to explore the details of the design matrices involved in Linear Mixed Models (LMM) with random effects associated with crossed and nested grouping factors. Of great interest to me, is also, ...
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0answers
19 views

How to distinguish between interval and categorical variables, and between continuous and count data?

I need to decide what statistical test to use to analyse my data, but I cannot decide which one to use because I'm not sure what kind of variables I have. In similar papers, they all use ANOVA, but I ...
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1answer
43 views

Glmm models -> AIC model selection -> Several models have the same “explanationary power” (similar AIC) -> now what?

So, I have a bunch of models, I'm using AIC for model selection, I don't have the exact numbers in front of me now but let's take for example: Model 1 - AIC = 100 Model 2 - AIC = 101; $\Delta$AIC = 1 ...
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1answer
48 views

Fixed effects of independent variables highly correlated with the intercept. Is it an issue?

In a linear mixed model run with lmer() I got this output for the correlation between fixed effects. My independent variable is almost perfectly correlated (...
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1answer
140 views

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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0answers
14 views

Have I adjusted for predictors I use as random effects?

I believe this is a rather straight forward question. I just read a research article in which it stated that: "[...] we used study centre as random effect, which also means that we adjusted for study ...
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0answers
44 views

Binomial vs. quasibinomial 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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0answers
16 views

Calculating proportion of variance explained by random effect in multinomial GLMM

I have a multinomial logistic GLMM with one random intercept. The number of response categories $C = 4$. Since a multinomial logit model consists of $C-1$ binomial logit models -- each pairing one non-...
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1answer
41 views

Different results from poisson glmer and glmmadmb when using emmeans (lsmeans)

Why would I be getting drastically different results from glmer and glmmadmbM for the same model when using emmeans? The results from summary() are the same. EMmeans glmer: ...
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0answers
18 views

Glmer fitted values differ from glm fitted values

I have data of proportion of flowering across several years and want to test a temporal trend in the flowering using a glmer, with proportion of flowering as response and year as predictor. I first ...
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0answers
27 views

linear mixed model gives wrong results

I am currently learning Stan (MCMC C++ engine with wrappers in python and R) and implemented a linear mixed model $y_{i,j} = \beta_0 + \mathbf{x}_{i,j}^T\beta + \alpha_i + \epsilon_{i,j},\ 1\leq i\...
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0answers
63 views

Convergence issues with GLMM from lme4

I originally posted this question in Stack Overflow but it was suggested that it might be more appropriate here. I am new to mixed models and am having some trouble getting my models to converge. I'...
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0answers
26 views

How to analyse overdispersed and autocorrelated data from a repeated measures design experiment (Mixed model time-series analysis)?

I have survival (number of alive+number of dead=total number) and productivity (total brood number) data on two ant species made to corxist in lab for 60 days. The question of interest is if group ...
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0answers
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How to recalculate variance/covariance matrix adding a overdispersion term in glmm?

When models suffer from overdispersion, a solution is to calculate the dispersion parameter (using c_hat or dispersion_glmer in R for example). Then multiply the variance/covarianze matrix of ...
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0answers
28 views

Accounting for spatial dependence in conditional logistic or GLM paired regression

I am studying the factors that influenced the location of pre-Euro American settlement travel routes. To do this I am using a paired used/available approach. I have points where trails were observed ...
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1answer
71 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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1answer
62 views

Confidence intervals for GLMM: bootstrap vs likelihood profile

I've built a relatively large negative binomial GLMM (~50000 observations, 10 covariates in conditional model, 1 covariate as zero-inflation model, 3 random intercepts (650, 26, 26 levels in each ...
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1answer
30 views

Infinite continuous response variable for logistic distribution? GLMM

When I would like to use the generalized linear mixed model (GLMM) for my data analysis, I would have to check the distribution of the response variable so as to decide the link-function. The result ...
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0answers
32 views

Interaction plot between categorical and quadratic continuous variable

I ran a GLMM model with a binomial response to analyse bear presence at feeding sites (0 = absent, 1 = present) within two years. My code is: ...
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0answers
22 views

GLMM negative binomial with a nested design

I have data collected from an experiment organized as: 6 blocks(waterbaths), of which three at temperature 1 and three at temperature 2. In each block there were 15 separated containers, 5 at salinity ...
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0answers
49 views

How to extract individual coefficient predictions from a model

I'm conscious this straddles the no-man's-land between stack overflow and cross validated but felt it was more applicable here. I've created a glmm logit model in R using the glmmTMB package. I'm ...
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0answers
76 views

Maximal glmm converges but removing interactions leads to model convergence problems

I'm running a large negative binomial glmm, with a lot of zeros in my response, using glmmTMB in R. My model converges when I ...
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0answers
87 views

Specifying nested random factor in emmeans from a gamlss object

I am trying to use the package emmeans with a gamlss object with a mixed model using a beta distribution. I am unsure as to the best way to use the emmeans function to include my nested random effects....
2
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0answers
115 views

Diagnostic residuals for Beta GLMM weighted by sample size (Meta-analysis) using glmmTMB

I am conducting a GLMM for a meta-analysis using the beta distribution with the package glmmTMB. My response variable is a vector of correlations (No exact 0 or 1), but Fisher’s transformation fails ...
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1answer
40 views

GLMM with non integer proportion?

I would need some help analyzing data that I'm not sure how to analyze. I tested ten times fifteen subjects in three different experimental conditions and my goal is to compare their behaviors between ...
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0answers
8 views

Need help deciding which glmm package to use for count data from pitfall trap data

I am relatively new to R and am still learning coding, so please bear with me if this is a simplistic or not clear. ABOUT THE DATA: I collected insects in pitfall traps near small streams, and I ...