FRESA.CAD (version 2.0.2)

residualForNeRIs: Return residuals from prediction

Description

Given a model and a new data set, this function will return the residuals of the predicted values. When dealing with a Cox proportional hazards regression model, the function will return the Martingale residuals.

Usage

residualForNeRIs(object,
	                 testData,
	                 Outcome,
	                 eta = 0.05)

Arguments

object

An object of class lm, glm, or coxph containing the model to be analyzed

testData

A data frame where all variables are stored in different columns, with the data set to be predicted

Outcome

The name of the column in data that stores the variable to be predicted by the model

eta

The weight of the contribution of the Martingale residuals, or 1 - the weight of the contribution of the classification residuals (only needed if object is of class coxph)

Value

• A vector with the residuals (i.e. the differences between the predicted and the real outcome)

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)
	# Split the data set into train and test samples
	trainDataCancer <- dataCancer[1:(nrow(dataCancer)/2),]
	testDataCancer <- dataCancer[(nrow(dataCancer)/2+1):nrow(dataCancer),]
	# Get a Cox proportional hazards model using:
	# - 10 bootstrap loops
	# - Train data
	# - The ranked variables
	# - The Wilcoxon rank-sum test as the feature inclusion criterion
	cancerModel <- NeRIBasedFRESA.Model(loops = 10,
	                                    Outcome = "pgstat",
	                                    variableList = cancerVarNames,
	                                    data = trainDataCancer,
	                                    type = "COX",
	                                    testType= "Wilcox",
	                                    timeOutcome = "pgtime")
	# Get the residuals of the model
	# - In the test data
	# - Giving the same weight to the Martingale and classification residuals
	cancerModelRes <- residualForNeRIs(object = cancerModel$final.model,
	                                   testData = testDataCancer,
	                                   Outcome = "pgstat",
	                                   eta = 0.5)
	# Shut down the graphics device driver
	dev.off()