New answers tagged glmm
2
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R: How to fit a linear mixed model with a custom covariance structure for two random intercepts
Since each $(b_{1j}, b_{2j})$ (independently) follows the same bivariate normal distribution for $j \in \{1, \ldots, q\},$ your model is observationally equivalent (in the sense of an identical ...
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-1
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R: How to fit a linear mixed model with a custom covariance structure for two random intercepts
To fit your linear mixed model (LMM) with the desired covariance structure for the random effects in R, you’ll need to use a package that allows specification of the covariance structure explicitly. ...
2
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Generalized linear (mixed) model, binomial - help!
Regarding your second question, Is it ok to present these proportion plots, I would say yes, this is ok, but your plot is missing error bars. As you may know, most journal editors now ask for a ...
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0
votes
Correct binomial GLMM for temporal trends in species occurrences
It looks like you have a multivariate dataset, with a species composition matrix of 80 species by 120 sites. The question seems to be if there's difference in species composition, and which individual ...
- 502
0
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Varying dispersion parameter (=dispformula) in glmmTMB in R to account for heteroscedasticity that originates from one predictor
Heteroscedastic (Gaussian) Linear Mixed Model
I have written a post related to this (see here)
Conclusion:
glmmTMB can model heteroskedastic data via the ...
1
vote
Problem finding a distribution for my data (glmm)
A binomial might be fine as a start. You can conceptualise this as $N$ trials, where $N$ is the total number of species, and each trial is "is the $i$-th species here". Now, technically this ...
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0
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Problem finding a distribution for my data (glmm)
Agree with @DiogoBProvete that some form of Beta distribution would be appropriate. As they mention, Beta distributions don't include values of exactly zero or one (e.g. see here), so you have to do ...
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0
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Problem finding a distribution for my data (glmm)
This seems to be a case for a beta distribution, if your data varies continuously between 0 and 1. If it includes 0 and 1, you'd need to do a transformation, as the standard beta distribution doesn't ...
- 502
0
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Repeated measures within participant
In addition to my general answer on the distinction between random effects and correlated residuals, a practical example follows using the classical reaction-time data and the R package {nlme}. For a ...
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