Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).

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fitting behavioral data using glmmADMB while accounting for 2 repeated measured structures

I am attempting to analyze an experiment where I am testing for differences in agonistic behaviors (e.g. bite etc...) between two morphs of frogs sampled over 5 time periods (2 days; morning and ...
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7 views

Repeated measures in GLMM

I have a dataset in which individuals in some plant populations were measured over 3 consecutive years. My response variable is the reproduction of each individual. My fixed effects involve: one ...
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How can parameter expansion be applied to cox proportional hazard models with random effects?

Parameter expansion is used in various GLMMs to accelerate e.g. EM or Gibbs convergence. Is anybody aware of a paper/work which implements PX for CPH?
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23 views

Some doubts about using time random effect

I'm starting with lme4 and GLMM. Maybe this question can be basic for experimented researchers, but I'm still learning. I have a pooled data where every ...
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34 views

Convergence warnings in glmer

I am running a Generalized Linear Mixed Model in R 3.0.2 using lme4 1.1-7 for a dichotomous outcome variable (success, 0 = no, 1 = yes) ...
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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 ...
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Assumptions of Linear Mixed Model

I had data with repeated measurement and nested design. Conventional ANOVA requires strict control on homogeneity of variance and repeated measurement ANOVA requires assumption of sphericity. ...
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How to measure classification accuracy based on presence only data?

I have a binomial GLMM which I calibrated with data on recreational visits (presence) compared with random controls where no visits were recorded (absence). I generated the controls myself, whereas ...
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1answer
34 views

Assessing the fit of GLMM implimentation 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 implementation of Rasch family models, e.g.: ...
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1answer
47 views

Using interaction terms in an MCMCglmm

I am using MCMCglmm models in R, with hierarchically nested data. The basic structure of the data is as follows - each dyad is a unique combination of focal/other: ...
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26 views

MCMCglmm interpretation of effective sample size

I'm running a selection of MCMCglmm models in R, and have a basic question about the outputs. Based on what I've been reading, one indication that the model is mixing well is a large effective ...
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25 views

Testing GLMM residuals against specific families and link functions (R)

When running a GLMM in R with family=gaussian and link=identity, it's easy enough to test whether normality and homoscedasticity ...
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GLMM overfitting solutions

in GLMM faq page http://glmm.wikidot.com/faq there is a statement about overfitting: "One alternative (suggested by Robert LaBudde) is to "fit the model with the random factor as a fixed effect, get ...
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1answer
72 views

Testing whether random effects are normally distributed in R

I've been working on a GLMM in R and I see that an assumption of the test is that the random factor must be normally distributed (that is, unless you're using a package like ...
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1answer
47 views

Can someone look at my method for fitting a GEE to my data?

I’ve been doing some statistical analyses in R on some data. It’s for use in a manuscript I’m hoping to get published in a biological journal. Unfortunately, the tests I ended up having to run are ...
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1answer
25 views

model comparison when alternatives are not all nested within one another

I am running a glmm with three fixed effects: opponent 1 size ("1") opponent 2 size ("2") opponent 1 size - opponent 2 size ("diff") I am unable to run all three variables in the model at once ...
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59 views

Should I exclude random effects from a model if they are not statistically significant?

Should I include random effects in a model even if they aren't statistically significant? I have a repeated measures experimental design, in which each individual experiences three different ...
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82 views

How to fit Graded Response Model with lme4::glmer

Thanks to Rijmen et al.(2003), we can fit GRM to the data with lme4::glmer. I think Rasch model is straightforward, with ...
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1answer
96 views

Include nesting factor as fixed effect in a GLMM

I have the following GLMM: success ~ age + gender + group/task + (1 + group/task|school/subject), family = binomial I want to know whether participants' ...
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220 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: ...
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1answer
207 views

R lme4 1.1-7: REML=FALSE giving error “extra argument(s) ‘REML’ disregarded ”

I'm trying to fit a model with the function glmer (lmer4 1.1-7 package) in R using REML but I just get an error saying ...
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References for crossed random effects

Could you recommend some examples of published articles that used general mixed-models or glmer() function with only crossed random effects and no random slopes. Using glmer function, the model will ...
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23 views

Usage of rma.glmm() for random effect single group study

Below are first few rows of my data that need to be analysed. I would need to understand the relation between number of years and survival rate. My statistical teacher advised on using rma.glmm() ...
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45 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 ...
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269 views

Likelihood and estimates for mixed effects Logistic regression

First let's simulate some data for a logistic regression with fixed and random parts: ...
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1answer
54 views

correct use of Negative Binomial with a Geometric distribution in a mixed model (glmmPQL)

I am trying to fit a NB GLMM with a gemoetric distribution. I have come across very little information on this form of regression. And would like some pointers/reasurance. some literature is ...
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32 views

How to specify binomial glmm with random and correlation in R?

I am working on developing a model with a binomial response variable. My data consist of GPS points from tracked animals. The data set is large and contains 15,000 observations from 40 individuals. I ...
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2answers
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GLMM for SNA and non-independency data

I contact you because my case is particular and I don’t know much about GLMM. I have data of social networks (network metrics) of a nonhuman primate species. These data are by nature non independent ...
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40 views

Taking a random factor into account while measuring a proportion a low N

I need to be neat in measuring the success rate of a treatment. It is anyway pretty high. But as it is all about ecology, multpliying experiments is difficult. I have treated $N = 20$ individuals, ...
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39 views

lmmlasso does not work for p>n

I am using the following example from the R manual: ...
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127 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 ...
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112 views

Compare LMM GLMM (generalised linear mixed model, negative binomial) by numerical measure (AIC BIC, cross validation, R² squared) for model validation

How to compare results of generalized linear mixed model (GLMM, negative binomial) with a log transformed linear mixed model (multilevel, hierarchical) . I have a data set (counts), which is nested. ...
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124 views

R² (squared) from a generalized linear mixed-effects models (GLMM) using a negative binomial distribution

I try to compute the marginal and conditional R² for a GLMM using a negative binomial distribution by following the procedure recommended by Nakagawa & Schielzeth (2013) . Unfortunately, the ...
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102 views

How to use the Huber/White estimator of covariances in a generalized linear mixed model (glmmPQL) in R?

An analysis was implemented in SPSS 22 that uses the "Generalized Linear Mixed Models" feature of the program. Now I am looking for a way to port this to R. I use the glmmPQL() function of the MASS ...
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Specifying a glmm for panel data

I'm trying to predict counts based on variables sampled on a monthly basis as well as a few that are not related to time. In several places I've read that the MCMCglmm package in R would be ...
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26 views

differences between different notations in mixed effects model

I have never been so confused in my life. Could anyone please tell me what is the difference between the following mixed effects models: ...
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39 views

glm or glmm model with unequal variance

I am applying a GLM model with binomial family: glm(response ~ Treatment, family = binomial, data=dat) The only explaratory variable treatment is a categorical ...
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1answer
103 views

Linear mixed effect models with two independent variables

I am estimating a random intercept and a random slope model using the following R code. My dependent and independent variable are both continuous. ...
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R-squared for linear mixed effects model [duplicate]

I ran linear mixed effects model in R. model<-lmer(yld ~ rain + (1+rain|state),data=data,REML=FALSE) Is there any way I can generate an R-squared for the ...
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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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1answer
104 views

Measuring goodness of fit for mixed logistic regression model - inconsistent results from R squared and AUC

I am trying to assess the goodness-of-fit or accuracy of 6 generalised linear models. I first assessed this using AUC (calculated from function auc1 described here), and got results ranging from 0.65 ...
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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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222 views

Testing significance of a random effect glmmADMB model

Below is the output from a model of novel object test scores fit with the nbinom1 (quasi-Poisson) option in glmmADMB. I used ...
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How to account for multiple & varying amounts of observations per factor level & still retain info? As a random effect in GLMM or take the mean?

I would appreciate any help! Specifically I would like to know which option is best. Question Does Var2 influence Var1 in relation to the factor(Ind), and does Var3 & Var4 also have some effect. ...
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547 views

Q: plot glmm fixed and random effects (glmer in package lme4) using ggplot2

I am trying to visualize the results from a glmm that I ran with the lme4 package. ...
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1answer
115 views

Significance of the overall model GLMM using lme4

Maybe it's a basic question, but I'm learning about GLMM using the lme4 package. I'm confused about the way that I can know the significance the overall model using glmer. First, the random model is: ...
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464 views

Coefficients from glmer in R

In a mixed effect model where the intercept is random effect and the slope is fixed effect (see the code below), I understand the output of summary(glmer(...)). But ...
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50 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 ...
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82 views

Calculating random effects from a glmerMod object (r package lme4)

Using the lme4 package, how does ranef() calculate (or extract) estimates of random effects from a ...
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67 views

GLMM and two slopes

My outcome variable is binomial, and I have 11 independent variables and a time variable. The time variable has different slopes, so I fixed it to time-before and ...