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When to use glmer or R-lmer in lme4?
Basically, I’m trying to use the lme4 package to analyze my data, and the model looks something like (A ~ BCD) + (random effects term 1) + (random effects term 2). ‘A’ is a yes/no response, which, based on what I’ve read, indicates that I should use glmer (). However, my experiment uses repeated measures – each subject undergoes many trials.
How are the RMA and LME functions different?
The models fitted by the rma () function assume that the sampling variances are known. The models fitted by the lm (), lme (), and lmer () functions assume that the sampling variances are known only up to a proportionality constant. These are different models than typically used in meta-analyses.
When to use lmer in a linear model?
When running through lmer, it is significant again. If anyone can help shed some light on whether this makes sense or why it would be so, I’d appreciate it very much. lmer is used to fit linear mixed-effect models, so it assumes that the residual error has a Gaussian distribution.
Can a GLM be used to fit a binary model?
3) glm can fit a model for binary data without random effects. However, it is incorrect to compare a model fitted with glm to one fitted with glmer using a likelihood-based test because the likelihoods are not comparable.
When to use glmer in generalized linear model?
In this case you have to use glmer, which allow to fit a generalized linear mixed-effects model: these models include a link function that allows to predict response variables with non-Gaussian distributions.
When to use glmer in mixed effects model?
If your dependent variable A is a binary outcome (e.g. a yes/no response), then the error distribution is binomial and not Gaussian. In this case you have to use glmer, which allow to fit a generalized linear mixed-effects model: these models include a link function that allows to predict response variables with non-Gaussian distributions.
How to use lmer, glmer, random intercept in R?
I want to analysis using lmer, glmer in R. There is variables. In fact, I have more variables like that. I think, the data cases are sufficient. categorical independent variables (range) : sex (1,2), item (1:20), test (1:3);I used factor () random intercept, random slope, independent variables are same.