diagnosis.MTA: Diagnosis method for the Mahalanobis-Taguchi Adjoint (MTA)...
In MTSYS: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value References See Also Examples

diagnosis.MTA (via diagnosis) calculates the distance based on the unit space generated by MTA or generates_unit_space(..., method = "MTA") and classifies each sample into positive (TRUE) or negative (FALSE) by comparing the values with the set threshold value.

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## S3 method for class 'MTA'
diagnosis(unit_space, newdata, threshold,
  includes_transformed_newdata = FALSE)

`unit_space`	Object of class "MTA" generated by `MTA` or `generates_unit_space`(..., method = "MTA").
`newdata`	Matrix with n rows (samples) and p columns (variables). The data are used to calculate the desired distances from the unit space. All data should be continuous values and should not have missing values.
`threshold`	Numeric specifying the threshold value to classify each sample into positive (`TRUE`) or negative (`FALSE`).
`includes_transformed_newdata`	If `TRUE`, then the transformed data for `newdata` are included in a return object.

diagnosis.MTA (via diagnosis) returns a list containing the following components:

`distance`	Vector with length n. Distances from the unit space to each sample.
`le_threshold`	Vector with length n. Logical values indicating the distance of each sample is less than or equal to the threhold value (`TRUE`) or not (`FALSE`).
`threshold`	Numeric value to classify the sample into positive or negative.
`unit_space`	Object of class "MTA" passed by `unit_space`.
`n`	The number of samples for `newdata`.
`q`	The number of variables after the data transformation. q equals p.
`x`	If `includes_transformed_newdata` is `TRUE`, then the transformed data for `newdata` are included.

Taguchi, G. & Kanetaka, T. (2002). Engineering Technical Development in MT System - Lecture on Applied Quality. Japanese Standards Association. (In Japanese)

Taguchi, G., & Jugulum, R. (2002). The Mahalanobis-Taguchi strategy: A pattern technology system. John Wiley & Sons.

general_diagnosis.MT and MTA

# 40 data for versicolor in the iris dataset
iris_versicolor <- iris[61:100, -5]

unit_space_MTA <- MTA(unit_space_data = iris_versicolor,
                      includes_transformed_data = TRUE)

# 10 data for each kind (setosa, versicolor, virginica) in the iris dataset
iris_test <- iris[c(1:10, 51:60, 101:111), -5]

diagnosis_MTA <- diagnosis(unit_space = unit_space_MTA,
                           newdata = iris_test,
                           threshold = 0.5,
                           includes_transformed_newdata = TRUE)

(diagnosis_MTA$distance)
(diagnosis_MTA$le_threshold)