How to fitting a GLMM to a response variable?

How to fitting a GLMM to a response variable?

Fitting a binomial GLMM (glmer) to a response variable that is a proportion or fraction. I have some count data as a response variable and I want to measure how that variable changes with the proportional presence of something. In more detail, the response variable is counts of the presence of an insect species in a number of sites,…

How to plot A binomial GLMM with a proportional response variable?

How to plot (in R) a binomial GLMM with a proportional response variable [cbind (Successes, Failures) variable], and a continuous fixed factor? How to plot (in R) a binomial GLMM with a proportional response variable [analyzed using cbind (Successes, Failures)], and a continuous fixed factor?

How to regress percentages in a binomial GLM?

In eval (expr, envir, enclos) : non-integer #successes in a binomial glm! and the analysis of this (pilot) data gives weird answers as a result. I understand why the binomial family expects integers (yes-no counts), but it seems it should be OK to regress percentage data directly.

Is the binomial GLMM the right answer?

I cant seem to find a definitive answer on the net without still feeling slightly uncertain, and wish to make sure I am not making a mistake. Any help or insight into alternative methods on this would be much appreciated. The binomial GLMM is probably the right answer.

Which is the correct method for fitting a glmer?

The second would be a binomial GLMM ( glmer ) e.g. I believe the binomial glmer to be the correct method, however they produce fairly different results. I cant seem to find a definitive answer on the net without still feeling slightly uncertain, and wish to make sure I am not making a mistake.

How to check for GLMM residual distribution in R?

The DHARMa package in R offers the solution by using a simulation-based approach to check for the GLMM residual distribution and plot tests for normality, uniformity, and dispersion.

How to check overdispersion of binomial GLMMs, lme4?

If you did have N>1, the previously linked GLMM FAQ gives some guidance the overdisp_fun from there can be used; depending on your philosophy of model-building you can use a hypothesis test (e.g. p < 0.05 ?) or a rule of thumb (e.g. overdispersion factor > 1.1) to decide whether you should worry about it.