No. of nearest neighbours to be used, default value is 0.05*nrow(x)
cutoff
Percentile threshold used for distance, default value is 0.95
Method
Distance method, default is Euclidean
rnames
Logical value indicating whether the dataset has rownames, default value is False
boottimes
Number of bootsrap samples to find the cutoff, default is 100 samples
Value
Outlier Observations: A matrix of outlier observations
Location of Outlier: Vector of Sr. no. of outliers
Outlier probability: Vector of proportion of times an outlier exceeds local bootstrap cutoff
Details
nn computes average knn distance of observation and based on the bootstrapped cutoff, labels an observation as outlier. Outlierliness of the labelled 'Outlier' is also reported and it is the bootstrap estimate of probability of the observation being an outlier. For bivariate data, it also shows the scatterplot of the data with labelled outliers.
References
Hautamaki, V., Karkkainen, I., and Franti, P. 2004. Outlier detection using k-nearest neighbour graph. In Proc. IEEE Int. Conf. on Pattern Recognition (ICPR), Cambridge, UK.