PReMiuM (version 3.1.3)

plotPredictions: Plot the conditional density using the predicted scenarios

Description

Plots the conditional density for the predicted scenarios provided. It produces a pdf with a page for each predictive scenario provided. Each page has a plot of the predicted response, in the order as they were provided to the function. Note that fixed effects are not processed in this function. This function has been developed for Bernoulli, Normal and Survival response only. This function has been developed for Discrete and Normal covariates only.

Usage

plotPredictions(outfile, runInfoObj, predictions, logOR=FALSE)

Arguments

outfile

String. The name of the output PDF file. The default is "condDensity.pdf".

runInfoObj

An object of type runInfoObj which contains all the details about the run of profRegr.

predictions

An object of type predictions which contains all the details about the run of calcPredictions.

logOR

Whether to plot the response probability or log odds ratios. The default is FALSE and the response probability is plotted.

Value

The output is a plot in PDF format.

Authors

Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK Maintainer: Silvia Liverani

References

Silvia Liverani, David I. Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson (2015). PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes. Journal of Statistical Software, 64(7), 1-30. URL http://www.jstatsoft.org/v64/i07/.

Examples

## Not run: 
# # example with Bernoulli outcome and Discrete covariates
# inputs <- generateSampleDataFile(clusSummaryBernoulliDiscrete())
# # prediction profiles
# preds<-data.frame(matrix(c(
# 2, 2, 2, 2, 2,
# 0, 0, NA, 0, 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=10000, nBurn=10000, data=inputs$inputData, output="output", 
#  covNames=inputs$covNames,predict=preds,
#  fixedEffectsNames = inputs$fixedEffectNames)        
# dissimObj <- calcDissimilarityMatrix(runInfoObj)
# clusObj <- calcOptimalClustering(dissimObj)
# riskProfileObj <- calcAvgRiskAndProfile(clusObj)
# predictions <- calcPredictions(riskProfileObj,fullSweepPredictions=TRUE,fullSweepLogOR=TRUE)
# 
# plotPredictions(outfile="predictiveDensity.pdf",runInfoObj=runInfoObj,
#  predictions=predictions,logOR=TRUE)
# 
# ## End(Not run)