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

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Accounting for initial values of reponse variable in binomial GLM

R v.3.1.0. Mac OSX 10.9.5. Can somebody please recommend a method to account for initial values of a response variable in binomial GLMM? I am designing an ecological experiment, and am having a hard ...
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Zero-inflated mixed models with two-stage fitting

Zero-inflated models have a count component (Poisson/Neg. Binomial) and a zero component (logistic regression part). glmmADMB supports the zero-inflation feature but only through estimating a ...
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1answer
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Model selection: can I compare the AIC from models of count data between linear and poisson models?

I am modeling count data (with offset / exposure parameter). My modeling strategy is use of a Poisson model and a negative binomial regression model. I compare model AICs, which are about -760 for my ...
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1answer
47 views

Computing repeatability from overdispersed zero-inflated negative binomial GLMMM in R

I'm trying to compute repeatability of a count response variable from a Generalized linear mixed model with multiple fixed effects and individual ID as a random effect. I'm dealing with both ...
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GLMM with 2 insignificant variables has lower AIC or BIC compared to same model without those variables…?

I am having a hard time understanding what's going on in with my model selection, and why a model with two insignificant variables is getting chosen as the "best model" over a model without those two ...
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How to analyze nested, repeated and unbalanced questionnaires where the outcome is binary?

Background: My data was collected once a year for 6 years using the same questionnaire. All responses have a person-specific ID. 18 554 responses from 5 654 unique persons were collected from 7 ...
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14 views

How many random effects to specify in glmmADMB?

I am not sure if I could use 4 random effects in a glmmADMB model. According to Bolker et al. (TREE 24: 127-131, 2009) when there are more than 3 random effects MCMC should be used. However, I do not ...
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Can I get some help writing a full model? in R, using lme4, glmm, with overdispersion

I'm trying to see how treatments affect species call counts at different locations. But I am not sure what model formula I need to use. My data is organized in an excel file and looks like this: ...
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1answer
54 views

MATLAB2014b `fitglme` causes error on intermediate results

((This post is a duplicate from Stack Exchange as there was no response there)) MATLAB R2014b's library function fitglme is acting up. It seems to be producing ...
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95 views

Explained variation ($R^2$) from MCMC glmm - Nakagawa & Schielzeth 2013

This is a follow up to a question I asked previously, where I suggested that variance-covariance matrices could be used to derive correlations, which are then usable as estimates of how much each ...
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Confidence interval for psychometric binomial GLM model on more than one subject in R

I'm trying to estimate the confidence interval of a psychometric curve (binomial probit GLM), for a population (now only two subjects). Suppose I've subject "a" and subject "b", which performs ...
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Probit regression with misclassified binary dependent variable in R

Is there a generalized linear mixed-effects model implementation for R that could handle misclassified binary data? Unless I have overlooked something in the documentation, glmer with ...
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1answer
69 views

In what sense is the interpretation of coefficients in a GLMM subject-specific?

There is something I'm not quite understanding conceptually about the output from generalized linear mixed models. I have read that the target of inference in GLMMs is subject-specific. For example, ...
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175 views

Overdispersion and modeling alternatives in Poisson random effect models with offsets

I have run into a number of practical questions when modeling count data from experimental research using a within-subject experiment. I briefly describe the experiment, data, and what I have done so ...
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2answers
73 views

Standardize non-normal predictors before performing binomial GLMM using mean and sd?

I am planning to predict a binomial variable (1/0, a used point by an animal or point available to an animal in its range) using several continuous, distance-based predictor variables (distance to ...
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Problem running dredge function with glmmadmb model

I'm running the following model in R: ...
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21 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 ...
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15 views

choosing the best structure of the random effects in a GLMM [duplicate]

I am trying to choose the best random effect structure in a GLMM, before starting with the fixed terms. To do that I include all the fixed effect and their interactions (beyond optimal model) and ...
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lmer, effect of a ramdom factor in a covariate [closed]

hi I am doing a General Linear Mixed Model in R, lme4 package. I want to test the effect of a random effect on a covariate, as well as on the response variable. I thin that the command would be the ...
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Do I need more than one random slope?

When constructing a GLMM in R, do I need more than one random slope if I "see" that slopes differ for multiple continuous variables? In my case, I am analysing the number of plant species (...
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2answers
116 views

Can I include covariate as random effect in glmer?

I have a question regarding covariates in a GLMM. My model comprises a condition variable and a covariate. Crucially, my binomially distributed dependent variable can be interpreted only in dependency ...
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9 views

Clear explanation of pseudo likehood

In generalized linear mixed model (glimmix) parameters are estimated using pseudo likelihood. I was trying to understand how this type of likelihood calculated. Thanks !!!
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51 views

Looking for an intuitive and simple way of visualizing the effects of factors in a GLMM analysis

This is a conceptual question. I have data to which I fit a GLMM. The data are numerical observations (a metabolite concentration in the blood, hence defined over the positive real), obtained from 2 ...
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1answer
128 views

How to interpret “main effects” in a GLMM?

Recently, I asked a question about what procedure to use to analyse mixed data with dichotomous outcomes, see [here][1]. Now I started running some first analyses (mainly with SPSS, but I'll post the ...
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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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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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32 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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107 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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51 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 ...
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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
55 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
73 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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71 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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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
110 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
65 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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1answer
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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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129 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
132 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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457 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
417 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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31 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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69 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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320 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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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 ...