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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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How to assess two random effects and interpret

I want to study the impact of two numerical factors (A & B) on outcome C (binomial). I used glmer in lmer4 package, and my models were as 1) m1<-glmer(C ~ A+B+(1|subject)+(A+B-1|subject), ...
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18 views

Detecting over-parameterization in GLMM: number of observations to number of parameters ratio?

I'm using generalized linear mixed models (GLMM) to model the effect of several testing conditions on the binary outcome of a behavioral trial. There were 40 individual subjects in my experiment, and ...
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29 views

Why are conditional and marginal pseudo R^2 the same in this example?

Here is a reproducible example: The problem is that when I calculate Pseudo R^squared for the null model it gives 0.18 to conditional R^2, the variance explained by the random factor is not 0. However ...
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20 views

Repeated Measures to Create Bigger Dataset for ML?

Background: I work for a small health center, and we're interested in predicting adverse events like hospitalizations. We are not interested in generalizable knowledge (effect sizes of predictors, ...
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1answer
24 views

How do I estimate the mean of groups as well as subgroups within a group?

For example, I have three groups A, B, and C. A has two subgroups, A1 and A2. B and C don't have subgroups. I have multiple observations for each individual in these groups. I want to estimate the ...
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1answer
32 views

Random effects specification in modeling panel (longitudinal) data

I am fitting a negative binomial model with mixed effects on a dataset with repeated measures in time. Each observation is a province-year combination, meaning province 1 year 1, province 1 year 2, ......
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1answer
14 views

How can we calculate AIC from a negative binomial GLMM?

Our problem here described is to calculate AIC from a GLMM negbin. Our data compose by 2 Categorical variables (Yes/Not), 3 Numerical variables and our random factor, all without any NA. We want to ...
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1answer
31 views

lmer logit regression - unexpected coefficients

I have a simulated dataset of N observations over NGroup individuals with a covariate x1 and ...
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0answers
8 views

Repeated measures on subjects without all subjects present in each period

I am working on a dataset in which several individuals were observed over several periods. However not all individuals were observed at each period. Due to repeated measures on individuals I have to ...
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2answers
175 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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1answer
35 views

Scale and log10()?

I'm trying to create a mixed model and I've both scaled and logged (log10) some of the explanatory variables. Residuals of the model are looking good. I was wondering whether there are any issues ...
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17 views

Gamma GLMM with log-link: how to report “rate of change” from exponentiated coefficients?

In my current study, we have 4 outcome variables of interest. Three of them are analysed using LMMs and one of them is analysed using a gamma GLMM with log-link. Our interest is the amount of change ...
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1answer
37 views

Linear mixed effect models - Why is time specific variance under estimated?

Suppose you fit a linear mixed effect model $$y_{ij} = \mu + \beta^T x_{ij} + u_i + \epsilon_{ij}$$ where $u_i \sim N(0,\sigma^2)$ and $\epsilon_{ij} \sim N(0,\nu^2)$. I have noticed that when ...
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2answers
63 views

Beta regression estimates and confidence intervals on response scale

I'm using GLMMadmb for my beta regression. I'm having a bit of trouble. I understand beta regression uses the logit link function and I know how to get from logits to probability. Here's selected ...
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1answer
67 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 ...
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1answer
88 views

R glm() with a categorical response variable [duplicate]

I want to know whether I can use glm() with a categorical response variable, and if yes how exactly. I have a response variable with three unordered levels, and both categorical and continuous ...
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0answers
11 views

Independent variable distribution in GLMM

Must the input / independent variables in a generalized linear mixed model (GLMM) be normally distributed?
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1answer
26 views

Specify nested random effects [closed]

I have data from a field survey. The objective of the study is to relate number of seedling (respond variable, count data), flood duration (exploratory variable, categorical variable with 3 levels) ...
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0answers
18 views

Different significance in GLMM interactions

I am confused about different significance results obtained in GLMM's. First, my set of relevant variables: ...
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1answer
62 views

GLMM optimiser test - optimx.L-BFGS-B doesn't converge, but the rest do

I am running GLMM using lme4 in R for the first time. I have a complex model (with three main effects and four interactions), as well as a random intercept of ...
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1answer
40 views

Is Repeated Measures ANOVA appropriate for flower production over time?

I wanted to check here to see if a repeated measures ANOVA would be a good option to analyze my data, or if you have any other suggestions? I have read that GLMM is another option for time series data,...
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24 views

Which method to use when calculating the confidence interval of GLMM Gamma Regression with the lme4 package in R

I am fitting a GLMM with family gamma using the lme4 package in R. Below is a code example to simulate the gamma GLMM fitting. ...
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1answer
23 views

Equation for GLMM w crossed random effects and logit link function

I am working on a GLMM model with crossed random effects and I would like to write an equation from the output where the outcome is the probability rather than a log of the odds. ...
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40 views

How to do classification in mixed effect models in python. My data is nested into groups with binary outcome

Lets say I have 10 sellers (S1-S10). Each seller has 7 buyers which are different for each seller (B1-B7 for S1, B11-B17 for S2 and so on). Each Seller buyer combination has a product category (P1, P2....
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1answer
22 views

Why does PQL vs MCMLEs optimisation give wildly different variance estimates?

In order to better understand modelling GLMM's using R I decided to re-do the example given in this Introduction to GLMM Package using the salamander dataset (provided in the glmm package). The ...
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2answers
116 views

Using glmm() to fit a model with repeated measurements?

I want to fit a GLMM model using the glmm() function in R. Its documentation is given here. I'm struggling to understand how to fit it given the way it's described in the documentation. Specifically, ...
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1answer
22 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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16 views

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
113 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
30 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
10 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
160 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
70 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
40 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
11 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
166 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
32 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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56 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
239 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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20 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
44 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
35 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
67 views

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

I conducted my analysis in SPSS as follows: I fitted a generalized linear mixed model based on multinomial distribution with a logit link function. I did that using repeated measures longitudinal ...
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8 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
64 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
39 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
29 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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28 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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13 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, ...