mahalanobis_method {OutliersLearn} | R Documentation |
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
[Package OutliersLearn version 1.0.0 Index]