Takes a dataset and finds its outliers using combination of different method
OutlierDetection(x, k = 0.05 * nrow(x), cutoff = 0.95,
Method = "euclidean", rnames = FALSE, depth = FALSE,
dense = FALSE, distance = FALSE, dispersion = FALSE)
dataset for which outliers are to be found
No. of nearest neighbours to be used for for outlier detection using bootstrapping, default value is 0.05*nrow(x)
Percentile threshold used for distance, default value is 0.95
Distance method, default is Euclidean
Logical value indicating whether the dataset has rownames, default value is False
Logical value indicating whether depth based method should be used or not, default is False
Logical value indicating whether density based method should be used or not, default is False
Logical value indicating whether distance based methods should be used or not, default is False
Logical value indicating whether dispersion based methods should be used or not, default is False
Outlier Observations: A matrix of outlier observations
Location of Outlier: Vector of Sr. no. of outliers
OutlierDetection finds outlier observations for the data using different methods and based on all the methods considered, labels an observation as outlier(intersection of all the methods). For bivariate data, it also shows the scatterplot of the data with labelled outliers.
# NOT RUN {
OutlierDetection(iris[,-5])
# }
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