FRESA.CAD (version 2.2.0)

summary.bootstrapValidation_Bin: Generate a report of the results obtained using the bootstrapValidation_Bin function

Description

This function prints two tables describing the results of the bootstrap-based validation of binary classification models. The first table reports the accuracy, sensitivity, specificity and area under the ROC curve (AUC) of the train and test data set, along with their confidence intervals. The second table reports the model coefficients and their corresponding integrated discrimination improvement (IDI) and net reclassification improvement (NRI) values.

Usage

"summary"(object,
	        ...)

Arguments

object

An object of class bootstrapValidation_Bin

...

Additional parameters for the generic summary function

Value

performance

A vector describing the results of the bootstrapping procedure

summary

An object of class summary.lm, summary.glm, or summary.coxph containing a summary of the analyzed model

coef

A matrix with the coefficients, IDI, NRI, and the 95% confidence intervals obtained via bootstrapping

performance.table

A matrix with the tabulated results of the blind test accuracy, sensitivity, specificities, and area under the ROC curve

See Also

summaryReport

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
# 	# - Age as a covariate
# 	# - zIDI as the feature inclusion criterion
# 	cancerModel <- ForwardSelection.Model.Bin(loops = 10,
# 	                                           covariates = "1 + age",
# 	                                           Outcome = "pgstat",
# 	                                           variableList = cancerVarNames,
# 	                                           data = dataCancer,
# 	                                           type = "COX",
# 	                                           timeOutcome = "pgtime",
# 	                                           selectionType = "zIDI")
# 	# Validate the previous model:
# 	# - Using 50 bootstrap loops
# 	bootCancerModel <- bootstrapValidation_Bin(loops = 50,
# 	                                       model.formula = cancerModel$formula,
# 	                                       Outcome = "pgstat",
# 	                                       data = dataCancer,
# 	                                       type = "COX")
# 	# Get the summary of the bootstrapped model
# 	sumBootCancerModel <- summary.bootstrapValidation_Bin(object = bootCancerModel)
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)