benchmarking {FRESA.CAD}

Compare performance of different model fitting/filtering algorithms

Description

Evaluates a data set with a set of fitting/filtering methods and returns the observed cross-validation performance

Usage

BinaryBenchmark(theData = NULL, theOutcome = "Class", reps = 100, trainFraction = 0.5,
                referenceCV = NULL,referenceName = "Reference"
                ,referenceFilterName="Reference")
RegresionBenchmark(theData = NULL, theOutcome = "Class", reps = 100, trainFraction = 0.5,
                referenceCV = NULL,referenceName = "Reference"
                ,referenceFilterName="Reference")
OrdinalBenchmark(theData = NULL, theOutcome = "Class", reps = 100, trainFraction = 0.5,
                referenceCV = NULL,referenceName = "Reference"
                ,referenceFilterName="Reference")

CoxBenchmark(theData = NULL, theOutcome = "Class", reps = 100, trainFraction = 0.5,
                referenceCV = NULL,referenceName = "Reference"
                ,referenceFilterName="COX.BSWiMS")

Arguments

theData

The data frame

theOutcome

The outcome feature

reps

The number of times that the random cross-validation will be performed

trainFraction

The fraction of the data used for training.

referenceCV

A single random cross-validation object to be benchmarked or a list of CVObjects to be compared

referenceName

The name of the reference classifier to be used in the reporting tables

referenceFilterName

The name of the reference filter to be used in the reporting tables

Details

The benchmark functions provide the performance of different classification algorithms (BinaryBenchmark), registration algorithms (RegresionBenchmark) or ordinal regression algorithms (OrdinalBenchmark) The evaluation method is based on applying the random cross-validation method (randomCV) that randomly splits the data into train and test sets. The user can provide a Cross validated object that will define the train-test partitions.

The BinaryBenchmark compares: BSWiMS,Random Forest ,RPART,LASSO,SVM/mRMR,KNN and the ensemble of them in their ability to correctly classify the test data. Furthermore, it evaluates the ability of the following feature selection algorithms: BSWiMS or ReferenceCV, LASSO, RPART, RF/BSWiMS, IDI, NRI, t-test, Wilcoxon, Kendall, and mRMR in their ability to select the best set of features for the following classification methods: SVM, KNN, Naive Bayes, Random Forest Nearest Centroid (NC) with root sum square (RSS) , and NC with Spearman correlation

The RegresionBenchmark compares: BSWiMS,Random Forest ,RPART,LASSO,SVM/mRMR and the ensemble of them in their ability to correctly predict the test data. Furthermore, it evaluates the ability of the following feature selection algorithms: BSWiMS or referenceCV, LASSO, RPART, RF/BSWiMS, F-Test, W-Test, Pearson Kendall, and mRMR in their ability to select the best set of features for the following regression methods: Linear Regression, Robust Regression, Ridge Regression, LASSO, SVM, and Random Forest.

The OrdinalBenchmark compares: BSWiMS,Random Forest ,RPART,LASSO,KNN ,SVM and the ensemble of them in their ability to correctly predict the test data. Furthermore, it evaluates the ability of the following feature selection algorithms: BSWiMS or referenceCV, LASSO, RPART, RF/BSWiMS, F-Test, Kendall, and mRMR in their ability to select the best set of features for the following regression methods: Ordinal, KNN, SVM, Random Forest, and Naive Bayes.

The CoxBenchmark compares: BSWiMS, LASSO, BeSS and Univariate Cox analysis in their ability to correctly predict the risk of event happening. It uses cox regression with the four alternatives, but BSWiMS, LASSO are also compared as Wrapper methods.

Value

errorciTable

the matrix of the balanced error with the 95 CI

accciTable

the matrix of the classification accuracy with the 95 CI

aucTable

the matrix of the ROC AUC with the 95 CI

senTable

the matrix of the sensitivity with the 95 CI

speTable

the matrix of the specificity with the 95 CI

errorciTable_filter

the matrix of the balanced error with the 95 CI for filter methods

accciTable_filter

the matrix of the classification accuracy with the 95 CI for filter methods

senciTable_filter

the matrix of the classification sensitivity with the 95 CI for filter methods

speciTable_filter

the matrix of the classification specificity with the 95 CI for filter methods

aucTable_filter

the matrix of the ROC AUC with the 95 CI for filter methods

CorTable

the matrix of the Pearson correlation with the 95 CI

RMSETable

the matrix of the root mean square error (RMSE) with the 95 CI

BiasTable

the matrix of the prediction bias with the 95 CI

CorTable_filter

the matrix of the Pearson correlation with the 95 CI for filter methods

RMSETable_filter

the matrix of the root mean square error (RMSE) with the 95 CI for filter methods

BiasTable_filter

the matrix of the prediction bias with the 95 CI for filter methods

BMAETable

the matrix of the balanced mean absolute error (MEA) with the 95 CI for filter methods

KappaTable

the matrix of the Kappa value with the 95 CI

BiasTable

the matrix of the prediction Bias with the 95 CI

KendallTable

the matrix of the Kendall correlation with the 95 CI

MAETable_filter

the matrix of the mean absolute error (MEA) with the 95 CI for filter methods

KappaTable_filter

the matrix of the Kappa value with the 95 CI for filter methods

BiasTable_filter

the matrix of the prediction Bias with the 95 CI for filter methods

KendallTable_filter

the matrix of the Kendall correlation with the 95 CI for filter methods

CIRiskTable

the matrix of the concordance index on Risk with the 95 CI

LogRankTable

the matrix of the LogRank Test with the 95 CI

CIRisksTable_filter

the matrix of the concordance index on Risk with the 95 CI for the filter methods

LogRankTable_filter

the matrix of the LogRank Test with the 95 CI for the filter methods

times

The average CPU time used by the method

jaccard_filter

The average Jaccard Index of the feature selection methods

TheCVEvaluations

The output of the randomCV (randomCV) evaluations of the different methods

testPredictions

A matrix with all the test predictions

featureSelectionFrequency

The frequency of feature selection

cpuElapsedTimes

The mean elapsed times

cpuElapsedTimes

Author(s)

Jose G. Tamez-Pena

See Also

randomCV

Examples

	## Not run: 

		### Binary Classification Example ####
		# Start the graphics device driver to save all plots in a pdf format
		pdf(file = "BinaryClassificationExample.pdf",width = 8, height = 6)
		# Get the stage C prostate cancer data from the rpart package

		data(stagec,package = "rpart")

		# Prepare the data. Create a model matrix without the event time
		stagec$pgtime <- NULL
		stagec$eet <- as.factor(stagec$eet)
		options(na.action = 'na.pass')
		stagec_mat <- cbind(pgstat = stagec$pgstat,
		as.data.frame(model.matrix(pgstat ~ .,stagec))[-1])

		# Impute the missing data
        dataCancerImputed <- nearestNeighborImpute(stagec_mat)
        dataCancerImputed[,1:ncol(dataCancerImputed)] <- sapply(dataCancerImputed,as.numeric)	

		# Cross validating a LDA classifier.
		# 80
		cv <- randomCV(dataCancerImputed,"pgstat",MASS::lda,trainFraction = 0.8, 
		repetitions = 10,featureSelectionFunction = univariate_tstudent,
		featureSelection.control = list(limit = 0.5,thr = 0.975));

		# Compare the LDA classifier with other methods
		cp <- BinaryBenchmark(referenceCV = cv,referenceName = "LDA",
		                      referenceFilterName="t.Student")
		pl <- plot(cp,prefix = "StageC: ")

		# Default Benchmark classifiers method (BSWiMS) and filter methods. 
		# 80
		cp <- BinaryBenchmark(theData = dataCancerImputed,
		theOutcome = "pgstat", reps = 10, fraction = 0.8)

		# plot the Cross Validation Metrics
		pl <- plot(cp,prefix = "Stagec:");

		# Shut down the graphics device driver
		dev.off()

		#### Regression Example ######
		# Start the graphics device driver to save all plots in a pdf format
		pdf(file = "RegressionExample.pdf",width=8, height=6)

		# Get the body fat data from the TH package

		data("bodyfat", package = "TH.data")

		# Benchmark regression methods and filter methods. 
		#80
		cp <- RegresionBenchmark(theData = bodyfat, 
		theOutcome = "DEXfat", reps = 10, fraction = 0.8)

		# plot the Cross Validation Metrics
		pl <- plot(cp,prefix = "Body Fat:");
		# Shut down the graphics device driver
		dev.off()

		#### Ordinal Regression Example #####
		# Start the graphics device driver to save all plots in a pdf format
		pdf(file = "OrdinalRegressionExample.pdf",width=8, height=6)


		# Get the GBSG2 data
		data("GBSG2", package = "TH.data")

		# Prepare the model frame for benchmarking
		GBSG2$time <- NULL;
		GBSG2$cens <- NULL;
		GBSG2_mat <- cbind(tgrade = as.numeric(GBSG2$tgrade),
		as.data.frame(model.matrix(tgrade~.,GBSG2))[-1])

		# Benchmark regression methods and filter methods. 
		#30
		cp <- OrdinalBenchmark(theData = GBSG2_mat, 
		theOutcome = "tgrade", reps = 10, fraction = 0.3)

		# plot the Cross Validation Metrics
		pl <- plot(cp,prefix = "GBSG:");

		# Shut down the graphics device driver
		dev.off()

	
## End(Not run)