## 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:
# # - The default parameters
# md <- FRESA.Model(formula = Surv(pgtime, pgstat) ~ 1,
# data = dataCancer,
# var.description = cancerVarNames[,2])
# # Get a logistic regression model using
# # - The default parameters
# md <- FRESA.Model(formula = pgstat ~ 1,
# data = dataCancer,
# var.description = cancerVarNames[,2])
# # Get a logistic regression model using:
# # - redidual-based optimization
# md <- FRESA.Model(formula = pgstat ~ 1,
# data = dataCancer,
# OptType = "Residual",
# var.description = cancerVarNames[,2])
# # Rank the variables:
# # - Analyzing the raw data
# # - According to the zIDI
# rankedDataCancer <- univariateRankVariables(variableList = cancerVarNames,
# formula = "Surv(pgtime, pgstat) ~ 1",
# Outcome = "pgstat",
# data = dataCancer,
# categorizationType = "Raw",
# type = "COX",
# rankingTest = "zIDI",
# description = "Description")
# # Get a Cox proportional hazards model using:
# # - 10 bootstrap loops
# # - Age as a covariate
# # - zIDI as the feature inclusion criterion
# cancerModel <- ForwardSelection.Model.Bin(loops = 10,
# covariates = "1 + age",
# Outcome = "pgstat",
# variableList = rankedDataCancer,
# data = dataCancer,
# type = "COX",
# timeOutcome = "pgtime",
# selectionType = "zIDI")
# # Update the model
# uCancerModel <- updateModel.Bin(Outcome = "pgstat",
# VarFrequencyTable = cancerModel$ranked.var,
# variableList = rankedDataCancer,
# data = dataCancer,
# type = "COX",
# timeOutcome = "pgtime")
# # Remove not significant variables from the previous model:
# # - Using zIDI as the feature removal criterion
# reducedCancerModel <- backVarElimination_Bin(object = uCancerModel$final.model,
# Outcome = "pgstat",
# data = dataCancer,
# type = "COX",
# selectionType = "zIDI")
# # Validate the previous model:
# # - Using 50 bootstrap loops
# bootCancerModel <- bootstrapValidation_Bin(loops = 50,
# model.formula = reducedCancerModel$back.formula,
# Outcome = "pgstat",
# data = dataCancer,
# type = "COX")
# # Get the summary of the bootstrapped model
# sumBootCancerModel <- summary.bootstrapValidation_Bin(object = bootCancerModel)
# # Plot the bootstrap results
# plot(bootCancerModel)
# # Scale the C prostate cancer data
# dataCancerScale <- as.data.frame(scale(dataCancer))
# # Generate a heat map using:
# # - All the variables
# # - The scaled data
# hmAll <- heatMaps(variableList = rankedDataCancer,
# Outcome = "pgstat",
# data = dataCancerScale,
# outcomeGain = 10)
# # Generate a heat map using:
# # - The top ranked variables
# # - The scaled data
# hmTop <- heatMaps(variableList = rankedDataCancer,
# varRank = cancerModel$ranked.var,
# Outcome = "pgstat",
# data = dataCancerScale,
# outcomeGain = 10)
# # Get a new Cox proportional hazards model using:
# # - The top 5 ranked variables
# # - No bootstrapping
# # - Age as a covariate
# # - The zIDI as the feature inclusion criterion
# # - A train fraction of 0.8
# # - A 2-fold cross-validation in the feature selection and update procedures
# # - A 10-fold cross-validation in the model validation procedure
# # - An elimination p-value of 0.1
# cancerModelCV <- crossValidationFeatureSelection_Bin(size = 5,
# loops = 1,
# covariates = "1 + age",
# Outcome = "pgstat",
# timeOutcome = "pgtime",
# variableList = rankedDataCancer,
# data = dataCancer,
# type = "COX",
# selectionType = "zIDI",
# trainFraction = 0.8,
# trainRepetition = 2,
# CVfolds = 10,
# elimination.pValue = 0.1)
# # List the COX models
# cancerModelCV$formula.list
# # Shut down the graphics device driver
# dev.off()## End(Not run)
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