mahalanobis_method: mahalanobis_method

In OutliersLearn: Educational Outlier Package with Common Outlier Detection Algorithms

mahalanobis_method

Description

Detect outliers using the Mahalanobis Distance method

Usage

mahalanobis_method(inputData, alpha, tutorialMode)

Arguments

inputData	Input Data dataset that will be processed (with or not the step by step explanation) to obtain the underlying outliers. It must be a data.frame type.
alpha	Significance level alpha. This value indicates the proportion that it is expected to be outliers out of the dataset. It has to be in the range from 0 to 1
tutorialMode	if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier)

Value

None, does not return any value

Author(s)

Andres Missiego Manjon

Examples

inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
mahalanobis_method(inputData, 0.7, FALSE); #Can be set to TRUE

OutliersLearn documentation built on June 22, 2024, 10:23 a.m.