FRESA.CAD (version 2.2.0)

predictForFresa: Linear or probabilistic prediction

Description

This function returns the predicted outcome of a specific model. The model is used to generate linear predictions. The probabilistic values are generated using the logistic transformation on the linear predictors.

Usage

predictForFresa(object,
	                testData,
	                predictType = c("prob", "linear"))

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

predictType

Prediction type: Probability ("prob") or linear predictor ("linear")

Value

A vector with the predicted values

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)
# 	# 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
# 	# - zIDI as the feature inclusion criterion
# 	# - First order interactions
# 	cancerModel <- ForwardSelection.Model.Bin(loops = 10,
# 	                                           Outcome = "pgstat",
# 	                                           variableList = cancerVarNames,
# 	                                           data = trainDataCancer,
# 	                                           type = "COX",
# 	                                           timeOutcome = "pgtime",
# 	                                           selectionType = "zIDI",
# 	                                           interaction = 2)
# 	# Predict the outcome of the test data sample
# 	predTest <- predictForFresa(object = cancerModel$final.model,
# 	                            testData = testDataCancer,
# 	                            predictType = "prob")
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)