plotRiskDistribution: Function to plot histogram of risks separated for individuals...
In PredictABEL: Assessment of risk prediction models

Description Usage Arguments Value See Also Examples

Function to plot histogram of risks separated for individuals with and without the outcome of interest.

1 2	plotRiskDistribution(data, cOutcome, risks, interval, rangexaxis, rangeyaxis, plottitle, xlabel, ylabel, labels, fileplot, plottype)

`data`	Data frame or numeric matrix that includes the outcome and predictor variables.
`cOutcome`	Column number of the outcome variable.
`risks`	Risk of each individual. It is specified by either a vector of risk scores or a vector of predicted risks.
`interval`	Size of the risk intervals. For example, `interval=.1` will construct the following intervals for predicted risks: `0-0.1, 0.1-0.2,..., 0.9-1`.
`rangexaxis`	Range of the x-axis. Specification of `rangexaxis` is optional.
`rangeyaxis`	Range of the y-axis.
`plottitle`	Title of the plot. Specification of `plottitle` is optional. Default is "Histogram of risks".
`xlabel`	Label of x-axis. Specification of `xlabel` is optional. Default is "Risk score".
`ylabel`	Label of y-axis. Specification of `ylabel` is optional. Default is "Percentage".
`labels`	Labels given to the groups of individuals without and with the outcome of interest. Specification of `labels` is optional. Default is `c("Without outcome", "With outcome")`.
`fileplot`	Name of the output file that contains the plot. The file is saved in the working directory in the format specified under `plottype`. Example: `fileplot="plotname"`. Note that the extension is not specified here. When `fileplot` is not specified, the plot is not saved.
`plottype`	The format in which the plot is saved. Available formats are wmf, emf, png, jpg, jpeg, bmp, tif, tiff, ps, eps or pdf. For example, `plottype="eps"` will save the plot in eps format. When `plottype` is not specified, the plot will be saved in jpg format.

The function creates the histogram of risks separated for individuals with and without the outcome of interest.

plotROC, riskScore

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2

# 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 the size of each interval
interval <- .05
# specify label of x-axis
xlabel <- "Predicted risk"
# specify label of y-axis
ylabel <- "Percentage"
# specify range of x-axis
xrange <- c(0,1)
# specify range of y-axis
yrange <- c(0,40)
# specify title for the plot
maintitle <- "Distribution of predicted risks"
# specify labels
labels <- c("Without outcome", "With outcome")

# produce risk distribution plot
plotRiskDistribution(data=ExampleData, cOutcome=cOutcome,
risks=predRisk, interval=interval, plottitle=maintitle, rangexaxis=xrange,
rangeyaxis=yrange, xlabel=xlabel, ylabel=ylabel, labels=labels)