bootstrapValidation_Bin: Bootstrap validation of binary classification models

Description

This function bootstraps the model n times to estimate for each variable the empirical distribution of model coefficients, area under ROC curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI). At each bootstrap the non-observed data is predicted by the trained model, and statistics of the test prediction are stored and reported. The method keeps track of predictions and plots the bootstrap-validated ROC. It may plots the blind test accuracy, sensitivity, and specificity, contrasted with the bootstrapped trained distributions.

Usage

bootstrapValidation_Bin(fraction = 1,
	                    loops = 200,
	                    model.formula,
	                    Outcome,
	                    data,
	                    type = c("LM", "LOGIT", "COX"),
	                    plots = TRUE)

Arguments

fraction

The fraction of data (sampled with replacement) to be used as train

loops

The number of bootstrap loops

model.formula

An object of class formula with the formula to be used

Outcome

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

data

A data frame where all variables are stored in different columns

type

Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")

plots

Logical. If TRUE, density distribution plots are displayed

Value

data: The data frame used to bootstrap and validate the model
outcome: A vector with the predictions made by the model
blind.accuracy: The accuracy of the model in the blind test set
blind.sensitivity: The sensitivity of the model in the blind test set
blind.specificity: The specificity of the model in the blind test set
train.ROCAUC: A vector with the AUC in the bootstrap train sets
blind.ROCAUC: An object of class roc containing the AUC in the bootstrap blind test set
boot.ROCAUC: An object of class roc containing the AUC using the mean of the bootstrapped coefficients
fraction: The fraction of data that was sampled with replacement
loops: The number of loops it took for the model to stabilize
base.Accuracy: The accuracy of the original model
base.sensitivity: The sensitivity of the original model
base.specificity: The specificity of the original model
accuracy: A vector with the accuracies in the bootstrap test sets
sensitivities: A vector with the sensitivities in the bootstrap test sets
specificities: A vector with the specificities in the bootstrap test sets
train.accuracy: A vector with the accuracies in the bootstrap train sets
train.sensitivity: A vector with the sensitivities in the bootstrap train sets
train.specificity: A vector with the specificities in the bootstrap train sets
s.coef: A matrix with the coefficients in the bootstrap train sets
boot.model: An object of class lm, glm, or coxph containing a model whose coefficients are the median of the coefficients of the bootstrapped models
boot.accuracy: The accuracy of the mboot.model model
boot.sensitivity: The sensitivity of the mboot.model model
boot.specificity: The specificity of the mboot.model model
z.NRIs: A matrix with the z-score of the NRI for each model term, estimated using the bootstrap train sets
z.IDIs: A matrix with the z-score of the IDI for each model term, estimated using the bootstrap train sets
test.z.NRIs: A matrix with the z-score of the NRI for each model term, estimated using the bootstrap test sets
test.z.IDIs: A matrix with the z-score of the IDI for each model term, estimated using the bootstrap test sets
NRIs: A matrix with the NRI for each model term, estimated using the bootstrap test sets
IDIs: A matrix with the IDI for each model term, estimated using the bootstrap test sets
testOutcome: A vector that contains all the individual outcomes used to validate the model in the bootstrap test sets
testPrediction: A vector that contains all the individual predictions used to validate the model in the bootstrap test sets

Details

The bootstrap validation will estimate the confidence interval of the model coefficients and the NRI and IDI. The non-sampled values will be used to estimate the blind accuracy, sensitivity, and specificity. A plot to monitor the evolution of the bootstrap procedure will be displayed if plots is set to TRUE. The plot shows the train and blind test ROC. The density distribution of the train accuracy, sensitivity, and specificity are also shown, with the blind test results drawn along the y-axis.

Examples

Run this code

	## 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")
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)

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