plotDiscriminationBox: Function for box plots of predicted risks separately for...
In PredictABEL: Assessment of Risk Prediction Models

Description Usage Arguments Details Value References See Also Examples

The function produces box plots of predicted risks for individuals with and without the outcome of interest and calculates the discrimination slope.

1 2	plotDiscriminationBox(data, cOutcome, predrisk, labels, plottitle, ylabel, fileplot, plottype)

`data`	Data frame or matrix that includes the outcome and predictors variables.
`cOutcome`	Column number of the outcome variable.
`predrisk`	Vector of predicted risks.
`labels`	Labels given to the groups of individuals without and with the outcome of interest. Specification of `label` is optional. Default is `c("Without disease", "With disease")`.
`plottitle`	Title of the plot. Specification of `plottitle` is optional. Default is "Box plot".
`ylabel`	Label of y-axis. Specification of `ylabel` is optional. Default is "Predicted risks".
`fileplot`	Name of the file that contains the plot. The file is saved in the working directory in the format specified under `plottype`. Example: `fileplot="name"`. 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. Foe example, `plottype="eps"` will save the plot in eps format. When `plottype` is not specified, the plot will be saved in jpg format.

The discrimination slope is the difference between the mean predicted risks of individuals with and without the outcome of interest. Predicted risks can be obtained using the fitLogRegModel and predRisk or be imported from other programs. The difference between discrimination slopes of two separate risk models is equivalent to (IDI) which is discussed in details in the reclassification function.

The function creates a box plots of predicted risks for individuals with and without the outcome of interest and returns the discrimination slope.

Yates JF. External correspondence: decomposition of the mean probability score. Organizational Behavior and Human Performance 1982;30:132-156.

reclassification, predRisk

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of 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 labels for the groups without and with the outcome of interest 
labels <- c("Without disease", "With disease")    

# produce discrimination box plot     
plotDiscriminationBox(data=ExampleData, cOutcome=cOutcome, predrisk=predRisk, 
labels=labels)