When to use GEE or GLMM?
If it is a conditional model, one should use a GLMM. If it is a marginal model, one can either use a GEE directly, or integrate the result from the GLMM (which I think is the way to go).
What is the difference between glm and GEE?
GEE is an extension of generalized linear models (GLM) for the analysis of longitudinal data. In this method, the correlation between measurements is modeled by assuming a working correlation matrix. Moreover, GLMM is an extension of GLM, inasmuch as it allows random effects in linear predictors.
Why use GEE model?
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes. Parameter estimates from the GEE are consistent even when the covariance structure is misspecified, under mild regularity conditions.
What are the advantages and disadvantages of GLMM and Gee?
So far I know that GEE requires a relatively large number of clusters, and that it produces profile curves that corresponds to no individual. GLMM on the other hand is not robust for violation of distributional assumptions, and is numerically intensive. Can you add more disadvantages of the two models ?
When to use Gee vs.glmm in a sentence?
Zurr et al. (2003) says more or less that both GEE and GLMM are used when the assumption of independence is violated.
What’s the difference between GLMM and Gee Cross?
So far I know that GEE requires a relatively large number of clusters, and that it produces profile curves that corresponds to no individual. GLMM on the other hand is not robust for violation of distributional assumptions, and is numerically intensive.
Is the GLMMs an extension of generalized linear regression?
Alternatively, you could think of GLMMs as an extension of generalized linear models (e.g., logistic regression) to include both fixed and random effects (hence mixed models). The general form of the model (in matrix notation) is: