if (FALSE) {
# example for Poisson outcome and Discrete covariates
inputs <- generateSampleDataFile(clusSummaryPoissonDiscrete())
runInfoObj<-profRegr(yModel=inputs$yModel,
xModel=inputs$xModel, nSweeps=10, nClusInit=15,
nBurn=20, data=inputs$inputData, output="output",
covNames = inputs$covNames, outcomeT = inputs$outcomeT,
fixedEffectsNames = inputs$fixedEffectNames)
dissimObj<-calcDissimilarityMatrix(runInfoObj)
clusObj<-calcOptimalClustering(dissimObj)
riskProfileObj<-calcAvgRiskAndProfile(clusObj)
clusterOrderObj<-plotRiskProfile(riskProfileObj,"summary.png")
}
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