Description
Methods in Mahalanobis-Taguchi (MT) System.
Description
Mahalanobis-Taguchi (MT) system is a collection of multivariate analysis methods developed for the field of quality engineering. MT system consists of two families depending on their purpose. One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y. (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods for the family of MT methods and one basic method for the family of T methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the MT method and the two-sided Taguchi (T1) method is for the family of T methods. In addition, the Ta and Tb methods, which are the improved versions of the T1 method, are included.
README.md
MTSYS
MTSYS provides a collection of multivariate analysis methods in Mahalanobis-Taguchi System (MTS), which was developed for the field of quality engineering. MTS consists of two families depending on their purpose. One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis and the other is a family of Taguchi (T) methods for forecasting.
Overview
The following methods are implemented.
A family of MT methods
- MT method
- MTA method
- RT method
A family of T methods
- T(1) method
- Ta method
- Tb method
For details, see the following referenses.
Installation
Install the release version from CRAN:
install.packages("MTSYS")
Or the development version from github
# install.packages("devtools")
devtools::install_github("okayaa/MTSYS")
Example
library(MTSYS)
# 40 data for versicolor in the iris dataset
iris_versicolor <- iris[61:100, -5]
unit_space_MT <- MT(unit_space_data = iris_versicolor)
# 10 data for each kind (setosa, versicolor, virginica) in the iris dataset
iris_test <- iris[c(1:10, 51:60, 101:111), -5]
diagnosis_MT <- diagnosis(unit_space = unit_space_MT, newdata = iris_test,
threshold = 4)
(diagnosis_MT$le_threshold)
#> 1 2 3 4 5 6 7 8 9 10
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 51 52 53 54 55 56 57 58 59 60
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 101 102 103 104 105 106 107 108 109 110 111
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
References
- Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) A review and analysis of the Mahalanobis-Taguchi system. Technometrics, 45(1), 1-15. <doi:10.1198/004017002188618626>
- Kawada, H., and Nagata, Y. (2015) An application of a generalized inverse regression estimator to Taguchi's T-Method. Total Quality Science, 1(1), 12-21. <doi:10.17929/tqs.1.12>