What are the assumptions of mixed-effects models?
Formally, the assumptions of a mixed-effects model involve validity of the model, independence of the data points, linearity of the relationship between predictor and response, absence of measurement error in the predictor, homogeneity of the residuals, independence of the random effects versus covariates (exogeneity).
What is the assumption 4 of Poisson regression?
Assumption 4: The mean and variance of the model are equal. This is a result of the assumption that the distribution of counts follows a Poisson distribution. For a Poisson distribution the variance has the same value as the mean. If this assumption is satisfied, then you have equidispersion.
Why do we have equidispersion in Poisson regression?
This is a result of the assumption that the distribution of counts follows a Poisson distribution. For a Poisson distribution the variance has the same value as the mean. If this assumption is satisfied, then you have equidispersion. However, this assumption is often violated as overdispersion is a common problem.
Is the variance of a Poisson distribution the same as the mean?
For a Poisson distribution the variance has the same value as the mean. If this assumption is satisfied, then you have equidispersion. However, this assumption is often violated as overdispersion is a common problem. Example: Poisson Regression in R. Now we will walk through an example of how to conduct Poisson regression in R. Background
What are the assumptions of a mixed effects logistic?
I’ve tested for overdispersion (using blmeco::dispersion_glmer ()) and the estimates do not appear to be overdispersed, but what are the other assumptions that are made when using this type of model that I should test – does anyone know of a comprehensive list somewhere, especially in a format that I could cite in a scientific PhD thesis?