R: Generate a report of the results obtained using the...
summary.bootstrapValidation_Bin
R Documentation
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
## S3 method for class 'bootstrapValidation_Bin'
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
Author(s)
Jose G. Tamez-Pena and Antonio Martinez-Torteya
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)