The following CV questions also discuss this material: Difference between generalized linear models & generalized linear mixed models in SPSS; When to use generalized estimating …

The following CV questions also discuss the relationship between GEE & GLiMMs: What is the difference between generalized estimating equations and GLMM; When to use generalized …

Fit the full GLMM. Insufficient computer memory o r too slow: reduce model complexity. If estimation succeeds on a subset of the data, try a more efficient estimation algorithm (e.g. …

Apr 13, 2022 · If you want to model individual bears as random effects then you have a mixed model, potentially a GLMM (generalized linear mixed model). A GAM (generalized additive …

A GLMM was considered but given that the outcome variable is dive depth there is no obvious distribution candidate for a GLMM. On thing to consider is whether the outcome is bounded.

Jan 14, 2020 · The interpretation is the same as for a generalised linear model, except that the estimates of the fixed effects are conditional on the random effects. Since this is a linear mixed …

Jun 18, 2019 · I have a test dataset with repeated measures, different individuals sampled at different time points, here measured in days. I want to know if I should use a GLMM or a LMM …

Mar 17, 2023 · Hoping to get some clarification on my understanding of interaction terms in a GLM model I have produced. I have written the following model interactionmodel <- lme …

Jul 24, 2020 · How to interpret GLMM results? Ask Question Asked 5 years, 4 months ago Modified 5 years, 4 months ago

After removing the outliers, the overdispersion is solved. So now I have three models that seem to be good to go. The first model is GLMM with negative binomial distribution. The 2nd model is …