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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Including matched variables in regression models

I've been trying to search in the literature to see whether it makes sense to adjust for the variables I used to create matched pairs. To give context, I have a population of schizophrenia patients ...
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Handling 0s in a generalized linear model---climate data

I am using a generalized linear mixed model for analyzing climate data and incidence of a disease variable. The data follows a gamma distribution. But I am getting the following error when I am ...
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45 views

How to interpret the random effect of a random slope model?

I designed an experiment to observe the shading effect in the distribution of 2 species of crabs through time. So basically I have 4 levels of shading (no shade, 20%, 50%, and 80%) with 7 ID to each (...
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GLMM with 1 predictor, still necessary to do Chi-Square with Anova in R e.g.?

I frequently use the Anova function in R to test if any predictors in a GLMM are significant. Is this necessary if I only have one predictor. Can I just use the significance of the predictor instead? ...
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Performing GLMM on community data with manyglm

I'm analysing some arthropod community data with generalised linear mixed models (GLMMs), using the manyglm function from the mvabund package. This data has arthropods sampled from multiple trees in ...
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How to include parameters from a variogram to account for spatial autocorrelation with a GLMM?

One of my supervisors told me to try to estimate the parameters from a variogram and use them as random effects in a GLMM to account for spatial autocorrelation, and I did not really get the point of ...
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What should i use lme or glm? (Biostatistics, diversity)

I am trying to work on the data of my master thesis. I have to create some model, but I am stuck due to my lack of knowledge in statistics (I am working on it). My data are 58 sample points each has ...
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Interpreting parameters of zero-inflation model with random intercept

I had previously asked if parameters of a Poisson model with a random intercept can be interpreted marginally or if it has to be conditional on the random effects. Based on the answer I received, they ...
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Why do the standardized residuals of a general linear mixed model using transformed data show a negative slope? [duplicate]

My data is no. of individuals of a certain age-class in a group, and it is right skewed. When I did a general linear mixed model with the number of individuals in a group as a dependent variable and a ...
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Is a GLMM applicable in a non-full factorial experimental design with two dependent fixed effects?

At the moment I plan the statistics for an upcoming experiment in Biology with two fixed effects whose effect on a fitness parameter of a parasite I would like to test. The two fixed effects are the ...
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Assessing treatment effect with GLMM logit-link

I am currently trying to assess whether integrated treatments (pesticide (Pest) or landscape modification (Veg)) had an effect on mite infestation rates of small rodents. The data is designed as a 5 ...
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Random effects are not centered in mixed effects logistic regression model

Random effects are usualy modelled as normally distributed with zero mean. Thus I would expect that the mean of the estimated random intercepts is close to zero. However, in my example this mean is ...
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Generalized Linear Mixed Model: Random slope inverts effect

I struggle with the analysis of my very skewed data with linear mixed models in R. Since the original data is for actual research, I can't share it with you, but I have created a fake dataset, that ...
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1answer
50 views

How to correctly transform estimated marginal means and SEs from binomial (logit) model?

Let's say I have this simple GLMM model in R: model = glmer(correct ~ treatment + (1|id), data = DATA, family = binomial(link=logit)), where correct is my dependent ...
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Options when model complexity and separation causes non-convergence in logistic regression

I have created an example data set here My data represent the presence/absence of a particular animal species (data$outcome) and measurements of trees. I would like ...
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qPCR Mixed Effects Model Singularity Problem

I'm a relative amateur to R but I am trying desperately to understand all of the statistics/terminology in order to make the most accurate results I can (I'm from a neuroscience background, so... baby ...
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Definition of weights in meta analysis using random effects generalized linear mixed models?

my understanding of statistics is pretty basic and i have reached my limit with the following. I am trying to find how the weights of single estimates are defined when using GLMM to calculate pooled ...
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Using GLMM/GAM to predict species abundance based on environmental variables

I hope someone out here can help me. I have some species sighting data and some associated environmental variables, looking something similar to this (a few more enviro variables): ...
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56 views

How to model negative zero-inflated continuous data?

I am currently trying to apply a model (family = gaussian) to an indicator of sentiment score, values range from -4 to +2, is zero-inflated and is continuous. I want to look whether there is a ...
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30 views

How to build a GLMM that observes the years since experimental design was established?

Hello, my first question, quite individual, so I find it difficult to relate already answered questions to mine. I have observed the vegetation development in forests of 5 different areas (...
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BLUPs and MCMCglmm

I have run a univariate MCMCglmm using a Poisson distribution, where I have the trait as the response variable, approx. 4 fixed effects and ID as a random effect. I have read tutorials online, which ...
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47 views

How to calculate CI for Median Odds Ratio?

According to Austin et al (2016) the median odds ratio (MOR) is defined by $\exp(\sqrt{2\sigma^2} \times \Phi^{-1}(0.75))$ , where $\Phi^{-1}$ denotes the inverse of the standard normal cumulative ...
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Is it better to overcome model singularity or overdispersion? … or to run a Bayesian glmer?

We are using a dataset extracted from a predation measurement experiment. My main question is: Do individuals catch more prey when they have tumors? We measure different control and tumoral ...
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Get the log likelihood from glmer with binomial data (and not only a proportional one)

The logLik.merMod manual page states that: logLik: Log-likelihood at the fitted value of the parameters. Note that for GLMMs, ...
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Multilevel Model where certain values for a predictor are exclusive to some participants

I am running a study that has a sample that is hierarchical. There will be 250 participants each making 50 decisions. The decisions participants make is the outcome variable, and is binary. So, ...
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Multiple groups' mean comparison with GLMM on unbalanced data with heteroscedasticity (glht)

I work in R with a data set containing a variable of interest (rv), a grouping factor (gg) and a random factor (...
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How to include phylogeny in GLMM? (not community ecology)

I have a dataset derived from a multi-species experiment that I carried out. I have 30 different plant species from several families and 3 geographical groups. I applied 6 different treatments to 5 ...
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Is sequential reduction approximation of likelihood/importance sampling more accurate than adaptive gaussian quadrature or monte-carlo?

https://cran.r-project.org/web/packages/glmmsr/vignettes/glmmsr-vignette.pdf Is the approximation of the likelihood in glmm by sequential reduction approximation or importance sampling more accurate ...
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Is roger-kenward not applicable to MCMCglmm as MCMCglmm is most accurate approximation to integral?

http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html This mentions MCMCglmm is more accurate than anything else, i.e. Laplace approx, adaptive-quadrature (AGQ), PQL. Does this make Kenward-Roger ...
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Should “kenward-roger”/“satterthwaite” degrees of freedom be used with “GLMM” fitted with penalized quasilikelihood"? [duplicate]

Should kenward-roger degrees of freedom correction be used with generalized linear mixed models fitted with penalized quasilikelihood/"PQL"? There's no reason it isn't implementable but the ...
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report negative biomial models (glmer.nb) in APA-Format: log-link?

So far I fitted my generalised mixed model with a negative binomial distribution (glmer.nb) and I like to report the results now. The output looks like this: ...
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Can proportional binomial regression be used in piecewise structural equation modelling (piecewiseSEM)?

I'm using generalized linear mixed models from the lme4 package to do structural equation modelling using piecewiseSEM. It works ...
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What distribution to use for left-skewed data in generalized linear mixed models (for use with structural equation modelling)?

I'm trying to run a GLMM with a response variable that is left-skewed. Eventually this model will form part of a piecewise structural equation model (using piecewiseSEM). I have data from 480 plots, ...
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145 views

How should I deal with spatial autocorrelation in beta GLMM (glmmTMB)? (Bird diversity)

I am trying to compare certain a taxonomic diversity index (for bird communities) calculated for squares in a map grid system (a total of 34 squares) for two different years: 1998 and 2018. My ...
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20 views

Unsure about Choosing Fixed and Random Effects in Generalized Linear Mixed Model (Repeated Measures)

I'm building a linear mixed model to forecast a discrete outcome variable customers (which can take on negative values, it's not count data) using a set of ...
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37 views

Positive z score but lower mean value

I ran a GLMM to check the increase or decrease of the dependent variable on two fixed effects (condition "Presence and condition "Absence") as: ...
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64 views

Meaning of the weight argument in glmer and lmer

I have been looking into how to use the weight argument of glmer/lmer to represent "frequency" weights. I was ...
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Why does uncorrelated random slope and intercept not convergence, but correlated random slope and intercept does converge?

In contrast to what I expected, I am able to fit a model with a correlated random intercept and slope, but not the same model with an uncorrelated random intercept and slope term. I was under the ...
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1answer
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Appropriate model that considers both treatment and risk factors for outcome

Consider a retrospective study to be planned with 200 patients with 5 risk factors (such as age, disease score = disease severity, hypertension etc.) which may affect (a) outcome as such (older ...
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1answer
56 views

What are marginal and conditional errors in a GLMM?

I'm modifying my ecology paper according to what the reviewers suggested but I have an issue with one statistical-related question. I ran a GLMM in lme4 on R to model the presence/absence of a certain ...
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1answer
58 views

Nakagawa's R2: what does it tell practice?

I am having a hard time figuring what Nakagawa's R² really "means". I understand that in simple linear regressions, R² indicates the amount of variance in the dependent variable explained by ...
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9 views

confidence bands of average models with multiple fixed effects

I wonder whether there is a practical approach to computing sensible confidence intervals for averaged models (such as models obtained in R using the ...
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2answers
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Do I gain any information from removing the random intercept model?

I am fitting a mixed effect model to data from a behavioural task where each participant performed the task multiple times, trying to predict a binomial response, something like the formula below: ...
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1answer
42 views

GLMM for unbalanced design, nested random factor or fixed factor?

The Goal Determine if the abundance of ectos differ between sites. The set up The data is percent abundance of ectomycorrhizal fungi from soil samples. There are ten soil samples per plot and there ...
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Best way to deal with count data going from 1 to 3?

I want to perform a model with multiple independent variables and 2 random effects to see their effects on clutch size. This is a plot of my response variable. My model ...
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1answer
88 views

Poisson distribution complications with proportions, GLMM

In order to analyse which factors have greater weight in the proportion of incidence (number of infected inidivuals against total individuals) difference within different habitats a Generalized linear ...
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28 views

GLMM, repeated measures count data and Poisson regression: not positive definite, scaling, and convergence issues

Summary Using the lme4::glmer package I am trying to run a Poisson regression model with fixed effect, random intercept, and random slope. I have watched many tutorials and it seemed like this was ...
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1answer
61 views

AIC to determine optimal degrees of freedom for natural spline in GLMM?

Is it appropriate to use AIC to determine the optimal degrees of freedom for a natural spline? I have measured 200 animals at six points in time. My data look like below. ...
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18 views

Hurdle models in R: sign of the hurdle component coefficients [duplicate]

I used to fit hurdle models with pscl::hurdle, I now use glmmTMB. The coefficients of the zero component of pscl::hurdle have the opposite sign of the coefficients of the zero-inflation component of ...
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1answer
38 views

Error message when running generalised mixed effect model

I'm using glmer() in package lme4 and I want to do a mixed effect model to see how my predictor variables contribute to changes in fish abundance. My data looks like this: data <- data.frame(Year =...

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