#simulate a dataset with continuous data
dataset <- matrix(nrow = 1000 , ncol = 200)
dataset <- apply(dataset, 1:2, function(i) runif(1, 1, 100))
#the target feature is the last column of the dataset as a vector
target <- dataset[,200]
results <- testIndFisher(target, dataset, xIndex = 44, csIndex = 100)
#>results
# $pvalue
# [1] 0.5553586
#
# $stat
# [1] 0.01869107
#
# $flag
# [1] 1
#
# $stat_hash
# NULL
#
# $pvalue_hash
# NULL
#require(gRbase) #for faster computations in the internal functions
#define class variable (here tha last column of the dataset)
target = 200;
#run the SES algorithm using the testIndFisher conditional independence test
sesObject <- SES(target , dataset , max_k=3 , threshold=0.05 , test="testIndFisher");
#print summary of the SES output
summary(sesObject);
#plot the SES output
plot(sesObject, mode="all");
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