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

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When fitting a GLMM, is the predicted value for any success or all successes or what?

I am relatively new to multilevel modeling and have just been given an assignment that uses a generalized linear mixed effects model. The outcome is smoking status (1=yes, 0=no) measured at three ...
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21 views

Cross validation before or after stepwise modeling [duplicate]

I have a dataset of 1931 observations and I intend to predict a binary outcome out of that. There is a list of 128 predictors (both binary and continuous). First I ran logistic regression modeling ...
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7 views

General advice on modeling clustered data

I have biomass data from ~100 sites which I wish to relate to site environmental characteristics, and then based on those relationships predict biomass (gm2) across synopic layers of surrogate data in ...
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26 views

Partially paired data, what kind of analysis to use?

I am struggling to figure out what kind of test to use for this analysis. I have 2 populations of hosts and parasites (two locations, A and B). I tested for host preference of parasites toward host A ...
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17 views

Variance component model for longitudinal data

I have a dataset with fixed and random effects, sampled over time (body phenotypes under fixed stimulations). Generally speaking, I'd like to construct a variance component/ partitioning model to ...
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2answers
91 views

GLMM validation: weird qq & fit vs residual plots

I'm encountering problems with the results of a glmer model (lme4-package). Im trying to answer the question, whether a beaver ...
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21 views

Estimating latent probability variables from binary response data (logit GLMM)

Question: How can I calculate the standard error of an estimate derived from two coefficients of a logit GLMM? We're studying the effect of a categorical condition ('volume', 3 levels) on two latent ...
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29 views

Can complete separation between a continuous predictor and a random effect cause failure to converge in a logit GLMM?

I’m running a logit mixed-effects model on binary data with a 2x2 within-subjects design, with subjects and items as crossed random effects, and the two independent variables deviation-contrast coded. ...
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1answer
29 views

confidence intervals around lines from glmer in lme4

I have spent a large amount of time trying to figure out how to generate a desired plot, and was wondering if any one can help. The plot is to illustrate an interaction between 'time' and 'group' on a ...
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27 views

Generalized linear model with random effects for skewed data

I'd like to use SPSS Generalized Linear Model to analyze a dataset of insects collected from one particular species of vegetables. I have following variables: NUMBER (number of insects collected) ...
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39 views

Marginal and Conditional $R^2$ for GLMM

I am trying to calculate $R^2$ (variance explained) for a set of data using GLMM's, and . Here's some dummy data. ...
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28 views

glmm for complete randomized block design with repeated measurement

I want to analyze count data from a complete randomized block design using a glmm. Observational plots are organized in 2 blocks. Within these blocks I have 5 different tratements which are replicated ...
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55 views

Error when using mixed function in afex package on GLMM

I have been using lme4 in order to fit an overdispersion model and GLMM to my data as shown below. This seems to work fine. ...
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14 views

Distribution-specific variance component to use with R-squared for ordinal logistic GLMM

I think this should be straightforward, though I cannot find an answer after digging around a lot within work by Nakagawa et al. on $R^2$ values for GLMMs. My question is similar to that posed before ...
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1answer
44 views

Translate glmer (lme4) model specification into MCMCglmm

I am having some computational trouble estimating the following model with the glmer function in lme4: ...
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52 views

Quasipoisson or negative binomial glmm with differing dispersion by group

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

Is GzLMM with linear linking function identical to (G)LMM?

Should I hesitate to report a "generalized linear mixed model with linear linking function (and assumption of a normally distributed target)" as simply a "(general) linear mixed model" in a ...
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11 views

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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27 views

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
57 views

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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68 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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58 views

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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25 views

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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23 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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80 views

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
64 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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129 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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28 views

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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24 views

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
114 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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231 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
106 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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44 views

Problem running dredge function with glmmadmb model

I'm running the following model in R: ...
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52 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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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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37 views

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
163 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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53 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
156 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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21 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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10 views

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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33 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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147 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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111 views

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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21 views

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
79 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.: ...