#simulate a dataset with continuous data
dataset <- matrix(runif(100 * 100, 1, 100), ncol = 100 )
#the target feature is the last column of the dataset as a vector
target <- dataset[, 100]
dataset <- dataset[, -100]
testIndReg(target, dataset, xIndex = 44, csIndex = 50)
testIndReg(target, dataset, xIndex = 44, csIndex = 50, robust = TRUE)
testIndRQ(target, dataset, xIndex = 44, csIndex = 50)
#require(gRbase) #for faster computations in the internal functions
#define class variable (here tha last column of the dataset)
#run the SES algorithm using the testIndReg conditional independence test
sesObject <- SES(target, dataset, max_k = 3, threshold = 0.05, test = "testIndReg");
sesObject2 <- SES(target, dataset, max_k = 3, threshold = 0.05, test = "testIndRQ");
#print summary of the SES output
summary(sesObject);
summary(sesObject2);
#plot the SES output
plot(sesObject, mode = "all");
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