## Not run:
# inputs <- generateSampleDataFile(clusSummaryBernoulliDiscrete())
#
# # prediction profiles
# preds<-data.frame(matrix(c(0, 0, 1, 0, 0,
# 0, 0, 1, NA, 0),ncol=5,byrow=TRUE))
# colnames(preds)<-names(inputs$inputData)[2:(inputs$nCovariates+1)]
#
# # run profile regression
# runInfoObj<-profRegr(yModel=inputs$yModel, xModel=inputs$xModel,
# nSweeps=100, nBurn=1000, data=inputs$inputData, output="output",
# covNames=inputs$covNames,predict=preds)
#
# # postprocessing
# dissimObj <- calcDissimilarityMatrix(runInfoObj)
# clusObj <- calcOptimalClustering(dissimObj)
# riskProfileObj <- calcAvgRiskAndProfile(clusObj)
# clusterOrderObj <- plotRiskProfile(riskProfileObj,"summary.png",
# whichCovariates=c(1,2))
# output_predictions <- calcPredictions(riskProfileObj,fullSweepPredictions=TRUE)
#
# # example where the fixed effects can be provided for prediction
# # but the observed response is missing
# # (there are 2 fixed effects in this example).
# # in this example we also use the Rao Blackwellised predictions
#
# inputs <- generateSampleDataFile(clusSummaryPoissonNormal())
#
# # prediction profiles
# predsPoisson<- data.frame(matrix(c(7, 2.27, -0.66, 1.07, 9,
# -0.01, -0.18, 0.91, 12, -0.09, -1.76, 1.04, 16, 1.55, 1.20, 0.89,
# 10, -1.35, 0.79, 0.95),ncol=5,byrow=TRUE))
# colnames(predsPoisson)<-names(inputs$inputData)[2:(inputs$nCovariates+1)]
#
# # run profile regression
# runInfoObj<-profRegr(yModel=inputs$yModel,
# xModel=inputs$xModel, nSweeps=100,
# nBurn=100, data=inputs$inputData, output="output",
# covNames = inputs$covNames, outcomeT="outcomeT",
# fixedEffectsNames = inputs$fixedEffectNames,predict=predsPoisson)
#
# # postprocessing
# dissimObj<-calcDissimilarityMatrix(runInfoObj)
# clusObj<-calcOptimalClustering(dissimObj)
# riskProfileObj<-calcAvgRiskAndProfile(clusObj)
# output_predictions <- calcPredictions(riskProfileObj,fullSweepPredictions=TRUE)
#
#
# # example where both the observed response and fixed effects are present
# #(there are no fixed effects in this example, but
# # these would just be added as columns between the first and last columns).
#
# inputs <- generateSampleDataFile(clusSummaryPoissonNormal())
#
# # prediction profiles
# predsPoisson<- data.frame(matrix(c(NA, 2.27, -0.66, 1.07, NA,
# -0.01, -0.18, 0.91, NA, -0.09, -1.76, 1.04, NA, 1.55, 1.20, 0.89,
# NA, -1.35, 0.79, 0.95),ncol=5,byrow=TRUE))
# colnames(predsPoisson)<-names(inputs$inputData)[2:(inputs$nCovariates+1)]
#
# # run profile regression
# runInfoObj<-profRegr(yModel=inputs$yModel,
# xModel=inputs$xModel, nSweeps=10,
# nBurn=20, data=inputs$inputData, output="output",
# covNames = inputs$covNames, outcomeT="outcomeT",
# fixedEffectsNames = inputs$fixedEffectNames,
# nClusInit=15, predict=predsPoisson)
#
# # postprocessing
# dissimObj<-calcDissimilarityMatrix(runInfoObj)
# clusObj<-calcOptimalClustering(dissimObj)
# riskProfileObj<-calcAvgRiskAndProfile(clusObj)
# output_predictions <- calcPredictions(riskProfileObj,fullSweepPredictions=TRUE)
#
# ## End(Not run)
Run the code above in your browser using DataLab