general_diagnosis.MT: General function to implement a diagnosis method for a family...
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value See Also Examples

general_diagnosis.MT is the general function that implements a diagnosis method for a family of Mahalanobis-Taguchi (MT) methods. Each diagnosis method of a family of MT methods can be implemented by setting the parameters of this function appropriately.

1 2	general_diagnosis.MT(unit_space, newdata, threshold, includes_transformed_newdata = FALSE)

`unit_space`	Object generated as a unit space.
`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.

A list containing the following components is returned.

`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 passed by `unit_space`.
`n`	The number of samples for `newdata`.
`q`	The number of independent variables after the data transformation. According to the data transoformation function, q may be equal to p.
`x`	If `includes_transformed_newdata` is `TRUE`, then the transformed data for `newdata` are included.

diagnosis.MT, diagnosis.MTA, and diagnosis.RT

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

# The following settings are same as the MT method.                          
unit_space <- general_MT(unit_space_data = iris_versicolor, 
                         generates_transform_function = 
                                            generates_normalization_function,
                         calc_A = function(x) solve(cor(x)),  
                         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 <- general_diagnosis.MT(unit_space = unit_space, 
                                  newdata = iris_test, 
                                  threshold = 4,
                                  includes_transformed_newdata = TRUE)
                              
(diagnosis$distance)
(diagnosis$le_threshold)