FRESA.CAD (version 2.2.0)

plot.bootstrapValidation_Res: 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

"plot"(x,
	     xlab = "Years",
	     ylab = "Survival",
	     ...)

Arguments

x

A bootstrapValidation_Res object

xlab

The label of the x-axis

ylab

The label of the y-axis

...

Additional parameters for the plot

See Also

plot.bootstrapValidation_Bin

Examples

	## Not run: 
# 	# 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 <- ForwardSelection.Model.Res(loops = 10,
# 	                                    Outcome = "pgstat",
# 	                                    variableList = cancerVarNames,
# 	                                    data = dataCancer,
# 	                                    type = "COX",
# 	                                    testType= "Wilcox",
# 	                                    timeOutcome = "pgtime")
# 	# Bootstrap the parameters of the previous model
# 	bootCancerModel <- bootstrapValidation_Res(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()## End(Not run)