Vince Carey
GEE solvers: case studies in DSC design and implementation
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GEEs (generalized estimating equations) provide a framework
for flexibly modeling clustered data. Generalized linear model
(GLM) components are used to specify marginal mean and variance
functions, and "working" covariance models specify multivariate
structure. The indefiniteness of the estimation and inference
framework is a basis for criticism from theoretical quarters
(Crowder, Bka 1995), but also a basis for interesting interface
design challenges and opportunities. I will comprehensively
describe the redesign of S4/R-targeted GEE solvers. Basic issues
include a) choice of language, b) representation of complex
clustered data structures to accommodate, e.g., responses and
predictors obtained on discordant timing sequences; c) inheritance
from and interoperation with existing tools for multivariate
modeling; d) choice of class/method decomposition to support
recognition of statistical data types; d) weak implementation
methods to ease retargeting to DSC platforms as they mature.
Peripheral issues include a) exploitation of XML-based literate
programming methods; b) automatic generation of javadoc-like
hypertext doc for S4/R classes and methods.



stvjc@gauss.med.harvard.edu
Channing Laboratory
Harvard Medical School