An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model.
|Author||Ridho Rahmadi [aut, cre], Perry Groot [aut, ths], Tom Heskes [aut, ths], Christoph Stich [ctb]|
|Date of publication||2017-04-05 03:27:52 UTC|
|Maintainer||Ridho Rahmadi <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
crossdata6V: Artificial cross-sectional data.
dataReshape: Reshape longitudinal data
getModelFitness: Scoring the given SEM models.
longiData4V3T: Artificial longitudinal data.
modelPop: Random SEM models.
plotStability: Plot of edge and causal path stability.
repairCyclicModel: Repairing a SEM model that is cyclic.
stableSpec: Stable specifications of constrained structural equation...
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