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

learn more… | top users | synonyms

0
votes
0answers
58 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 ...
0
votes
0answers
12 views

Extension of Marginal R2 to calculate percent variance explained by individual fixed effects

I'm using lmer in R to build a LMM on a large observational dataset. I want to be able to compare the response magnitude between my fixed and random effects, and please forgive me if I'm not ...
1
vote
0answers
24 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 ...
0
votes
0answers
12 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 ...
1
vote
1answer
54 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, ...
6
votes
0answers
109 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 ...
2
votes
2answers
46 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 ...
0
votes
0answers
15 views

Problem running dredge function with glmmadmb model

I'm running the following model in R: ...
2
votes
0answers
13 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 ...
0
votes
0answers
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 ...
1
vote
0answers
23 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 ...
5
votes
1answer
53 views

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 (...
2
votes
2answers
98 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 ...
0
votes
0answers
8 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 !!!
0
votes
0answers
50 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 ...
0
votes
1answer
118 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 ...
0
votes
0answers
10 views

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 ...
0
votes
0answers
13 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 ...
1
vote
0answers
9 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?
0
votes
0answers
30 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 ...
0
votes
0answers
86 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) ...
2
votes
0answers
40 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 ...
1
vote
0answers
50 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. ...
0
votes
0answers
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 ...
4
votes
1answer
43 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.: ...
1
vote
1answer
63 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: ...
0
votes
0answers
59 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 ...
1
vote
0answers
36 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 ...
4
votes
1answer
107 views

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 ...
1
vote
1answer
91 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 ...
0
votes
1answer
57 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 ...
1
vote
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 ...
5
votes
1answer
74 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 ...
0
votes
0answers
106 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 ...
1
vote
1answer
118 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' ...
0
votes
0answers
370 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: ...
1
vote
1answer
345 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 ...
0
votes
0answers
16 views

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 ...
0
votes
0answers
29 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() ...
2
votes
0answers
61 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 ...
7
votes
1answer
299 views

Likelihood and estimates for mixed effects Logistic regression

First let's simulate some data for a logistic regression with fixed and random parts: ...
0
votes
1answer
76 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 ...
1
vote
0answers
36 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 ...
0
votes
2answers
33 views

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 ...
3
votes
1answer
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, ...
1
vote
0answers
50 views

lmmlasso does not work for p>n

I am using the following example from the R manual: ...
1
vote
0answers
154 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 ...
0
votes
0answers
145 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. ...
0
votes
1answer
210 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 ...
0
votes
0answers
126 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 ...