FRESA.CAD (version 2.0.2)

plot.bootstrapValidationNeRI: Plot ROC curves of bootstrap results

Description

This function plots ROC curves and a Kaplan-Meier curve (when fitting a Cox proportional hazards regression model) of a bootstrapped model.

Usage

## S3 method for class 'bootstrapValidationNeRI':
plot(x,
	     xlab = "Years",
	     ylab = "Survival",
	     ...)

Arguments

x

A bootstrapValidationNeRI object

xlab

The label of the x-axis

ylab

The label of the y-axis

...

Additional parameters for the plot

See Also

plot.bootstrapValidation

Examples

# Start the graphics device driver to save all plots in a pdf format
	pdf(file = "Example.pdf")
	# Get the stage C prostate cancer data from the rpart package
	library(rpart)
	data(stagec)
	# Split the stages into several columns
	dataCancer <- cbind(stagec[,c(1:3,5:6)],
	                    gleason4 = 1*(stagec[,7] == 4),
	                    gleason5 = 1*(stagec[,7] == 5),
	                    gleason6 = 1*(stagec[,7] == 6),
	                    gleason7 = 1*(stagec[,7] == 7),
	                    gleason8 = 1*(stagec[,7] == 8),
	                    gleason910 = 1*(stagec[,7] >= 9),
	                    eet = 1*(stagec[,4] == 2),
	                    diploid = 1*(stagec[,8] == "diploid"),
	                    tetraploid = 1*(stagec[,8] == "tetraploid"),
	                    notAneuploid = 1-1*(stagec[,8] == "aneuploid"))
	# Remove the incomplete cases
	dataCancer <- dataCancer[complete.cases(dataCancer),]
	# Load a pre-stablished data frame with the names and descriptions of all variables
	data(cancerVarNames)
	# Get a Cox proportional hazards model using:
	# - 10 bootstrap loops
	# - The Wilcoxon rank-sum test as the feature inclusion criterion
	cancerModel <- NeRIBasedFRESA.Model(loops = 10,
	                                    Outcome = "pgstat",
	                                    variableList = cancerVarNames,
	                                    data = dataCancer,
	                                    type = "COX",
	                                    testType= "Wilcox",
	                                    timeOutcome = "pgtime")
	# Bootstrap the parameters of the previous model
	bootCancerModel <- bootstrapValidationNeRI(loops = 50,
	                                           model.formula = cancerModel$formula,
	                                           Outcome = "pgstat",
	                                           data = dataCancer,
	                                           type = "COX")
	# Plot the bootstrap results
	plot(x = bootCancerModel)
	# Shut down the graphics device driver
	dev.off()