GEE estimates population-average effects when observations come in clusters — repeated visits, siblings, matched sites — without assuming a full distribution for the random effects. Magic Stat runs the geepack-equivalent fit and reports the robust standard errors that GEE is used for.
GEE solves a set of estimating equations that depend on a mean model (a GLM with the family and link you choose) and a working correlation structure describing how observations inside a cluster move together. Its output is the population-average effect: how the average response changes with a predictor, across the whole population. Inference uses a sandwich (robust) estimator, so it stays valid even if the working correlation is not exactly right.
This is a different question from a mixed model's. A random-intercept mixed model gives a subject-specific effect (what happens to a given subject); GEE gives the marginal effect (what happens on average). For a linear model they coincide; for logistic and other non-linear links they do not, and choosing the wrong one is a real mistake, not a nuance.
The engine mirrors geepack's geeglm: the same Gauss–Seidel β → γ → α iteration, the same moment estimators for the correlation, the same convergence tolerance. It also reports when the fit did not converge — which the plain R summary does not do for you.
Families and links: gaussian · poisson · binomial · Gamma, with the links listed per family above.
Working correlation: independence · exchangeable · AR(1) · unstructured.
Clusters: defined by an ID column; the data are ordered by it before fitting, as geepack requires.
Scale: estimated by moments for gaussian and Gamma, fixed at the GLM value for poisson and binomial — the geepack convention.
GEE answers the population-average question and stays valid under a wrong working correlation when clusters are numerous. A mixed model answers the subject-specific question and can be preferable with few clusters or when you want variance components. For non-linear links the two effects genuinely differ; the choice is about which question you are asking.
independence is the conservative default and, with a robust sandwich, still gives valid inference. exchangeable fits repeated measures with no time order; AR(1) assumes evenly spaced, ordered measurements; unstructured is the most flexible and the most parameter-hungry.
A GLM assumes independent rows. If your rows are clustered, its standard errors are too small. GEE accounts for the within-cluster dependence through the working correlation and the robust variance.
Yes — unlike the plain R summary, Magic Stat shows a note when the iteration did not converge, so a non-converged fit is not presented as a clean result.
No. The estimation runs locally; only an anonymous installation counter is sent.
Mixed models → Generalized linear models → Logistic regression →