I am analyzing a survey where ~20,000 cases were sampled in ~1000 clusters. I would like to analyze the data using, for example, gam. What is the best way to account for the clustering? I've tried including the cluster ID as a factor in the model formula, but the default response is to try and estimate the unique effect of each cluster, which given 1000 clusters is impossibly time consuming. What I want instead is an estimate of the variance due to clusters, or perhaps an intraclass correlation, and cluster-adjusted standard errors for the effects of other variables in the model.

I expect I can account for clustering by using lme with clusters as a random effect, but then I can't use the flexible smooths available in gam.

If it's not possible to get both clustering and smooths, I may use gam and adjust the standard errors using an estimate of the design effect.

Many thanks for any advice,
Paul

Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University

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