# NOT RUN {
# specify dataset with outcome and predictor variables
data(ExampleData)
# fit a logistic regression  model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2
# obtain predicted risks
predRisk <- predRisk(riskmodel)
# specify column numbers of genetic predictors
cGenPred <- c(11:16)
# function to compute unweighted genetic risk scores
riskScore <- riskScore(weights=riskmodel, data=ExampleData, 
cGenPreds=cGenPred, Type="unweighted")
# specify range of x-axis
rangexaxis <- c(0,12)   
# specify range of y-axis
rangeyaxis <- c(0,1)     
# specify label of x-axis
xlabel <- "Risk score"     
# specify label of y-axis
ylabel <- "Predicted risk" 
# specify title for the plot
plottitle <- "Risk score versus predicted risk"
# produce risk score-predicted risk plot
plotRiskscorePredrisk(data=ExampleData, riskScore=riskScore, predRisk=predRisk, 
plottitle=plottitle, xlabel=xlabel, ylabel=ylabel, rangexaxis=rangexaxis, 
rangeyaxis=rangeyaxis)
# }
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