Logical value indicating whether the dataset has rownames, default value is False
cutoff
Percentile threshold used for depth, default value is 0.05
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
depthout computes depth of an observation using depthTools package and based on the bootstrapped cutoff, label 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
Johnson, T., Kwok, I., and Ng, R.T. 1998. Fast computation of 2-dimensional depth contours. In Proc. Int. Conf. on Knowledge Discovery and Data Mining (KDD), New York, NY. Kno