diagnosis.RT: Diagnosis method for the Recognition-Taguchi (RT) method
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value References See Also Examples

diagnosis.RT (via diagnosis) calculates the distance based on the unit space generated by RT or generates_unit_space(..., method = "RT") 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 'RT'
diagnosis(unit_space, newdata, threshold,
  includes_transformed_newdata = FALSE)

`unit_space`	Object of class "RT" generated by `RT` or `generates_unit_space`(..., method = "RT").
`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.RT (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 "RT" passed by `unit_space`.
`n`	The number of samples for `newdata`.
`q`	The number of variables after the data transformation. q is always 2.
`x`	If `includes_transformed_newdata` is `TRUE`, then the transformed data for `newdata` are included.

Taguchi, G. (2006). Objective Function and Generic Function (11). Journal of Quality Engineering Society, 14(2), 5-9. (In Japanese)

Huda, F., Kajiwara, I., Hosoya, N., & Kawamura, S. (2013). Bolt loosening analysis and diagnosis by non-contact laser excitation vibration tests. Mechanical systems and signal processing, 40(2), 589-604.

general_diagnosis.MT and RT

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

unit_space_RT <- RT(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_RT <- diagnosis(unit_space = unit_space_RT,
                          newdata = iris_test,
                          threshold = 0.2,
                          includes_transformed_newdata = TRUE)

(diagnosis_RT$distance)
(diagnosis_RT$le_threshold)