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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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 zero-...
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1k views

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 ...
6
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1answer
177 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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257 views

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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631 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 ...
5
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525 views

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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556 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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149 views

How does random variable nesting in GAMs work (mgcv)?

Consider me very new to the world of GAMs, I am actually using it out of necessity rather than by choice but I thought it could be a chance to learn something new anyway. Originally I had hoped to ...
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117 views

Mixed-effects logistic regression

I'm new to data analysis and I'm trying to perform a mixed-effect logistic regression. My data look like this: ...
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99 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: ...
4
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2k views

Random or fixed effects? GLM or GLMM?

I am interested in the behavioral response of floral visitors to a treatment, applied in a paired fashion within plants. That is, one stem on each plant receives the treatment, and another stem serves ...
4
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579 views

Including seasons and months into GLMM: should they be crossed or nested effects?

I have collected data from five consecutive fishing seasons (five factor levels). Each fishing season has five months within (five factor levels). Considering that I have a temporal correlation in my ...
4
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146 views

LRT for a Linear Mixed Model

I want to compare the likelihoods for a model with population structure (a mixed effect represented by a variance-covariance matrix) to one without population structure (represented by an identity ...
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5k views

How to validate a Poisson GLMM model?

I’m using the glmer function from the lme4 package in R to model species richness adjacent ...
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705 views

Quasipoisson or negative binomial glmm with differing dispersion by group

I have a set of count data, which look something like this: ...
4
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492 views

GLMM with 2 insignificant variables has lower AIC or BIC compared to same model without those variables…?

Background This post has been heavily edited from its previous version (three months ago). I am investigating habitat selection of 35 territorial wolves over several years of denning seasons (41 ...
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1k views

How can I evaluate spatial autocorrelation in a binomial GLMM?

Following Dormann et al 2007 Ecography, I have employed a GLMM approach in R to account for spatial autocorrelation in a binomial regression model (logistic regression) that does not have random terms....
4
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753 views

Data analysis : replication, pseudoreplication and mixed models

I have several questions concerning analysis of data, especially when there are replications and/or pseudoreplications. First, I read an example in « pseudoreplication is a pseudoproblem » where we ...
4
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1k views

Model diagnostics for a glmmPQL in R mixed-effects model

Several texts (both online and published books) have been reviewed prior to asking this. What diagnostics are accepted as best practise for a generalised linear mixed-effects model fitted in R using ...
4
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4k views

Weights argument in glmer() when predicting proportion data: why is it needed when all weights are around the same?

What do the weights argument in glmer refer to? I used sample sizes as weights with ...
4
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0answers
633 views

Obtaining predicted probabilities that include multiple random effects from mixed effects model

I'm running a mixed effects logit model with a binary response variable. The data are cross-national survey data, over multiple waves (i.e., World Values Survey). As such, the random effects specified ...
4
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4k views

GEE (or GLMM) in SPSS: Interpreting outputs and model selection

I am attempting to analyze my (experimental psych) data in SPSS, and I have a few questions regarding the kind of analysis I should be using (GEE or GLMM), how I should be interpreting the output, and ...
4
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861 views

Mixed-effect modeling with paired observations & bounded response variables

I am quite new in the field of mixed-effect modeling. For a beginner like me, I guess I combine several levels of complication in my analysis: paired observations & bounded response variables. I ...
3
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1answer
27 views

GLMM in seed germination study

I have an experimental design measuring germination of a single species of tree under different treatments. The treatments include; cattle grazing and no cattle grazing and rodents and no rodents. The ...
3
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112 views

How to deal with zero-inflated proportional data in GLMM?

I have proportional data, i.e. number of individuals out of 6 that choose a certain option in a multiple choice experiment, so there are just 7 possible outcomes for each option: 0/6; 1/6; 2/6; 3/6; 4/...
3
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1answer
48 views

Using GLMM to explain vegetation change as a function of change in soil parameters

I have a dataset that consists of vegetation datasets (species/abundancy tables) and soil parameters (tested for ten parameters per soil core). Three different rounds of these data are recorded in ...
3
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1answer
1k 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 ...
3
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83 views

Could interaction effects prevent GLMM's (lme4) from converging?

In the following analysis, I am exploring the association between health outcomes and a range of environmental and socioeconomic predictors. These data have been collected from 9 countries, over ...
3
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565 views

Can including an observation level random effect (OLRE) create nested random factors in a GLMM?

I have data from an observational study on insect parasitism rates from 42 sites. Some sites were sampled only once and others were sampled multiple times across different years. For each sampling ...
3
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92 views

Meta-analysis of response ratios using GLMM?

I'm reading a paper and I'm unsure how valid their methodology is, and I'm trying to find some information related to that. Basically, they're doing a meta-analysis of swallows and PCB exposure. They'...
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58 views

What are “parameter expanded models”?

Hadfield (2010) talks about "parameter expanded models" (page 11). I never heard of these models before and wonder what he means by this. In the Appendix E he writes more about "Parameter expansion" ...
3
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112 views

What happens when assumptions about the distribution of the latent variable in a GLMM are violated?

I am struggling with one assumption usually made when using linear mixed models: the nature of the distribution of the random factor. (The first part of the question is more about biology --whether ...
3
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0answers
294 views

Dealing with missing data in mixed-models with categorical variables

I have a data set which consists of a binary response, two variables of random effects and a dozen predictors, most of which are categorical. The data set has some 1400 observations, one third of ...
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0answers
703 views

glmmADMB- Pseudo R^2 and residual deviance criteria

I’ve been using glmmADMB to fit zero-inflated negative binomial models with a random effect (as far as I can tell, this is the only package that will allow me to do this). I have been trying to do ...
3
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0answers
582 views

Zero-inflated GLMM: correct use of AIC and comparing levels of fixed factor

I am struggling with a ZIGLMM in R. I have a data set on freshwater plant propagules (response variable) and the relation with the ecological state of ponds. Pond state is a categorical factor with ...
3
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0answers
885 views

Which assumptions do I need to check for a GLMM with a binary response (and how?)

I am modeling binomial responses using Generalized Linear Mixed Models with a nested random effect (not of interest, simply a control: year nested within location) and both categorical, count, and ...
3
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1answer
105 views

Question about application of GLMM with Poisson

I'm working on revising stats for a manuscript involving male reproductive success of deer. We measured three variables (body size, ...
3
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0answers
1k views

Test of significance for glmer

I would like to test the effect of a treatment ("crop") on species richness. I would rather use a glm for richness as it is a kind of count data. Besides, I have a nested sampling design (5 values ...
3
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0answers
123 views

How should I interpret/follow-up on mixed logistic regression (GLMM) diagnostics?

I have experimental data (n subjects = 64) in which the response variable, accuracy (0 or 1), was measured 9 times within subjects. My predictor is Condition (A vs B) measured between subjects. I ...
3
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0answers
112 views

Shall I use a random effect or not?

I need to see if in the case I am going to present it is worth to use a random effect or not. I carried out some bird counts from 9 elevated lookouts in an island. Just to orient you, these lookouts ...
3
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0answers
1k views

Fitting GLMMs for binomial data produces Error and Warning messages when using different fitting procedures in R.

I am trying to fit GLMM's to my data using the glmer function available in R's lme4 package. The data is available under the name block1and2 at: https://onedrive.live.com/redir?resid=1B727FC7180E87DF%...
3
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0answers
525 views

What GLM family and link function for “proportion of time”?

A simple question to which I don't seem to find the answer anywhere. I have a response variable duration of time spent doing A of individuals tested for $\text{...
3
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0answers
440 views

Should I use a beta-binomial or binomial glmm?

I have several data sets on wildlife disease incidence. One of the issues with my dependent variable is that it represents only current infection status, therefore 0 (no disease) can represent either ...
3
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0answers
423 views

Plotting fit for binomial lme

I've been asked by a reviewer on a manuscript to provide plots of a model fit for a binomial lme which is specified as follows: ...
3
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0answers
1k views

Analyzing split-plot design in lme4 in R

I have the data from a split-plot design where A is my whole plot fixed factor with two levels and B is my subplot fixed factor with 2 levels and C is my random block factor. How do I analyse these ...
3
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0answers
396 views

Binary interaction terms using lmer

I am trying to create a model using the lmer function. The model will contain the continuous response term "Average.profit" and explanatory terms "Type", "OtherType,...
3
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0answers
2k views

Are very large log likelihood and delta AIC values problematic for model selection?

I am using AICc for small sample sizes to compare 8 a priori models (including null model). I fitted my models using a GLMM due to the nested nature of my data and defined the family as 'poisson' ...
2
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0answers
62 views

Round values to fit a negative binomial using GLMMs

I am using a GLMM to model a network metric that ranges from 0 to 100 (contribution to nestedness) and trying to fit a model with four predictors and two random factors (species and site). I have 591 ...
2
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1answer
32 views

Power simulation on glmer.nb gave strange results

I would like to ask for solution or advice on strange result that glmer.nb from lme4 generated when simulating using simR package. I’m working on longitudinal gut microbiome abundance data (23 ...
2
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0answers
36 views

How are factor level coefficient estimates combined for total Wald chi-squared?

I have been working with mixed-effects models, with both continuous and categorical variables. I am interested in the combined effect of categorical variables with > 2 levels. I ran type II ANOVA on ...

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