dm.mahalanobis: Distance measure using Mahalanobis distance for outlier...

In DJL: Distance Measure Based Judgment and Learning

Distance measure using Mahalanobis distance for outlier detection

Description

Implements Mahalanobis distance measure for outlier detection. In addition to the basic distance measure, boxplots are provided with potential outlier(s) to give an insight into the early stage of data cleansing task.

Usage

dm.mahalanobis(data, from="median", p=10, plot=FALSE, v.index=NULL, layout=NULL)

Arguments

data	Dataframe
from	Datum point from which the distance is measured "mean" Mean of each column "median" Median of each column (default)
p	Percentage to which outlier point(s) is noted (default of 10)
plot	Switch for boxplot(s)
v.index	Numeric vector indicating column(s) to be printed in the boxplot. Default value of NULL will present all.
layout	Numeric vector indicating dimension of boxplots. Default value of NULL will find an optimal layout.

Value

$dist	Mahalanobis distance from from
$excluded	Excluded row(s) in row number
$order	Distance order (decreasing) in row number
$suspect	Potential outlier(s) in row number

Author(s)

Dong-Joon Lim, PhD

References

Hair, Joseph F., et al. Multivariate data analysis. Vol. 7. Upper Saddle River, NJ: Pearson Prentice Hall, 2006.

Examples

# Generate a sample dataframe
df <- data.frame(replicate(6, sample(0 : 100, 50)))

# go
dm.mahalanobis(df, plot = TRUE)

DJL documentation built on March 31, 2023, 9:05 p.m.