general_MT: General function to generate a unit space for a family of...
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value See Also Examples

general_MT is a (higher-order) general function that generates a unit space for a family of Mahalanobis-Taguchi (MT) methods. Each MT method can be implemented by setting the parameters of this function appropriately.

1 2	general_MT(unit_space_data, calc_A, generates_transform_function, includes_transformed_data = FALSE)

`unit_space_data`	Matrix with n rows (samples) and p columns (variables). Data to generate the unit space. All data should be continuous values and should not have missing values.
`calc_A`	Function that returns A in a quadratic form x'Ax. `calc_A` takes the transformed data as an (only) argument.
`generates_transform_function`	Function that takes `unit_space_data` as an (only) argument and returns a data transformation function. The data transformation function takes data as an (only) argument and returns the transformed data.
`includes_transformed_data`	If `TRUE`, then the transformed data are included in a return object.

A list containing the following components is returned.

`A`	q x q matrix calculated by `calc_A`.
`calc_A`	Function passed by `calc_A`.
`transforms_data`	Data transformation function generated from `generates_transform_function` based on `unit_space_data`.
`distance`	Vector with length n. Distances from the unit space to each sample.
`n`	The number of samples.
`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_data` is `TRUE`, then the transformed data are included.

MT, MTA and 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)
                         
(unit_space$distance)