# What is the main difference between GLM and GEE?

From my understanding, glm(not glmer) and GEE both handle binary values. But GEE is a marginal model and glmer is a random effects model (mixed model). So then what is the main difference between GLM (Not glmer) and GEE? Is GEE a longitudinal data version of GLM? Does it mean you can run 'glm' to only cross-sectional data?

• Welcome to CV. I'm not an expert in this field, but GLM and GEE are related topics. GLM are a way to extend linear regression, but don't state a way how to determine the generalized parameters. GEE are one possible way to do it. Maybe you could state the question a bit more explicit. – cherub Dec 13 '18 at 14:33
• have a look here: stats.stackexchange.com/questions/62939/… – chRrr Dec 13 '18 at 14:36
• glm per se uses a specific assumption on the data, for instance, it assumes that the (conditional) distribution of $y$ belongs to the exponential-family of distribution. estimation of the model parameters is then done by maximum likelihood. the estimators solve the so called score functions. a generalization of GLM is quasi-maximum-likelihood, where you just start with the score equations from GLM but don't assume that $y$ belongs to the exponential-family. QML score equations corresponds to the estimation equations in GEE. the latter wording is often used in cross-sectional context. – chRrr Dec 13 '18 at 14:52
• more specifically: in QML/GEE you weaken your assumptions on the data. you just assume that it still holds that $E(Y)= g(\alpha + \sum_{r=1}^K \beta_r X_r)$ as well as $Var(Y)= \sigma^2(g(\alpha + \sum_{r=1}^K \beta_r X_r))$, but don't assume that the distribution of $y$ is known anymore. – chRrr Dec 13 '18 at 15:04
• Check out McNeish et al (2017) [doi:10.1037/met0000078] for a discussion of the differences among MLM, GEE, and GLM for longitudinal data. – Noah Dec 13 '18 at 15:40