How to check overdispersion of binomial GLMMs, lme4?

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.

When to use lmer in binomial data analysis?

In the text book “The R Book”, (2007), pg 604, Crawley suggests using the lmer function with a binomial family for the analysis of binomial data where each participant contributes multiple responses (analagous to each of my chambers contributing multiple outcomes). Based on this example, I have used the following script for my data:

When to use glmer or GLM for binomial outcomes?

You can think of random intercepts as being part of the experimental design. On the other hand, if the random effect variance is very small and/or the inference or predictions for both the glm and glmer model are the largely the same, then it really doesn’t matter which you use anyway. For 2, yes, you should use glmer or glm for binomial outcomes.

How are GLM and lme4 used in multilevel analysis?

The tutorial uses the Thai Educational Data example in Chapter 6 of the book Multilevel analysis: Techniques and applications. Furthermore, the tutorial briefly demonstrates the multilevel extension of GLM models with the lme4 package in R. Lastly, more distributions and link functions in the GLM framework are discussed.

When does underdispersion occur in a binomial distribution?

Underdispersion exists when data exhibit less variation than you would expect based on a binomial distribution (for defectives) or a Poisson distribution (for defects). Underdispersion can occur when adjacent subgroups are correlated with each other, also known as autocorrelation.

Which is the opposite of underdispersion in statistics?

Underdispersion is the opposite of overdispersion. Underdispersion exists when data exhibit less variation than you would expect based on a binomial distribution (for defectives) or a Poisson distribution (for defects).

When does overdispersion occur in a p chart?

Overdispersion exists when data exhibit more variation than you would expect based on a binomial distribution (for defectives) or a Poisson distribution (for defects). Traditional P charts and U charts assume that your rate of defectives or defects remains constant over time.

Can a model based on a binomial distribution be overdispersed?

Models based on single parameter distributions like the binomial can be overdispersed or underdispersed, where the variance in the data is bigger or smaller, respectively, than the variance defined by the binomial distribution.

How does a binomial generalized linear mixed model work?

The imaginary study design that is the basis of my model has two different sizes of study units. This is a field experiment scenario, where multiple sites within a region are selected and then two plots within each site are randomly placed and a treatment assigned (“treatment” or “control”). You can think of “sites” as a blocking variable.

What is y / NUM _ Samp in a binomial model?

Note that y/num_samp is the proportion of plants that survived, which is what we are interested in. In binomial models in R you often use the number of successes and the number of failures (total trials minus the number of successes) as the response variable instead of the actual observed proportion.

How does Proc glimmix use a distribution to estimate parameters?

Proc glimmix uses a distribution to estimate model parameters; it does not fit the data to a distribution. The data values are not transformed by the link function; the link function converts the LS-means back to the data scale after being estimated on the model scale. Poisson Distribution Model for Count Data

Do you need to manually adjust GLMMs for unbalanced data?

Unless you have data that are severely unbalanced, or unless the data are structured that you have complete separation (all zeros or all ones for some combinations of predictor variables), GLMMs will handle unbalanced data fine; there’s no need for manual adjustment. Thanks for contributing an answer to Cross Validated!